The present disclosure relates generally to systems and methods for computer learning that can provide improved computer performance, features, and uses. More particularly, the present disclosure relates to systems and methods for robust and efficient blind super-resolution.
Deep neural networks have achieved great successes in many domains, such as computer vision, natural language processing, recommender systems, etc.
Image super-resolution (SR) refers to the process to recover high-resolution (HR) images from low-resolution (LR) inputs. It is an important image processing technique to enhance image quality which subsequently helps to improve high-level computer vision tasks. Recent models for blind image SR generally combine both kernel estimation and image restoration modules into an end-to-end training framework, where the estimated kernel or kernel features are fused with LR inputs and features for SR restoration. Although they are less sensitive to kernel estimation uncertainties, they may still not be robust to great variations in kernels and kernel estimation errors and the performance may drop significantly in real-world LR images.
Accordingly, what is needed are systems and methods for robust and efficient blind super-resolution.
In a first aspect, a computer-implemented method for blind super-resolution (SR) is provided. The method includes: in a first iteration of an iterative restoration-estimation process: receiving, at a super-resolution (SR) restorer in a blind SR model, an input image and an initial kernel feature vector to generate an intermediate recovered image, and generating, using a kernel estimator in the blind SR model, an updated kernel feature vector based on the intermediate recovered image and the LR image, where the blind SR model is trained, before deployment, using a variational kernel autoencoder (VKAE) with a training set including multiple image pairs, each image pair has a high-resolution (HR) image and a low-resolution (LR) image degraded from the HR image using a ground-truth (GT) kernel; continuing the restoration-estimation process for a plurality of iterations until a stop condition is met; and outputting a recovered image with a resolution higher than the input image by the SR restorer and a kernel feature vector by the kernel estimator.
In a second aspect, a computer-implemented method for blind super-resolution (SR) training is provided. The method includes: given a training set including multiple image pairs with each image pair having an original image and a transformed image transformed from the original image via a 2D transformation, outputting, using a blind SR model including a SR Restorer and a kernel Estimator linked iteratively, a first estimated kernel feature vector for the original image and a second estimated kernel feature vector for the transformed image in each image pair; decoding, using a kernel decoder in a variational kernel autoencoder (VKAE), the first estimated kernel feature vector and the second estimated kernel feature vector into a first estimated kernel and a second estimated kernel respectively, where the VKAE includes the kernel decoder and a kernel encoder that encodes a kernel into a kernel feature vector; obtaining an inversely transformed kernel by applying an inverse transformation of the 2D transformation to the second estimated kernel; obtaining a similarity between the first estimated kernel and the inversely transformed kernel; constructing a kernel-agnostic loss that includes the similarity; and training the blind SR model using at least the kernel-agnostic loss.
In a third aspect, a non-transitory computer-readable medium or media including one or more sequences of instructions is provided. The instructions, when executed by at least one processor, causes steps for blind super-resolution including: pre-training a variational kernel autoencoder (VKAE) using a kernel dataset including various blurring kernels, where the VKAE includes a kernel encoder to encode a blurring kernel from the kernel dataset into a kernel feature vector and a kernel decoder to reconstruct the kernel feature vector into a reconstructed burring kernel, a kernel loss between the burring kernel and the reconstructed burring kernel is used for pre-training; and training, with the pre-trained VKAE, a blind SR model using a training set including multiple image pairs, where each image pair has a high-resolution (HR) image and a low-resolution (LR) image degraded from the HR image using a ground-truth (GT) kernel, the blind SR model includes a SR Restorer and a kernel Estimator linked iteratively, the blind SR model is trained using steps including: outputting a recovered image by the SR Restorer and an estimated kernel feature vector by the kernel Estimator based on the LR image in each image pair; obtaining a restoration loss based on the recovered image and the HR image in each image pair; obtaining a kernel feature loss based on the estimated kernel feature vector and the kernel feature vector that is encoded, by the kernel encoder, from the GT kernel; and training the blind SR model using at least the restoration loss and the kernel feature loss.
References will be made to embodiments of the disclosure, examples of which may be illustrated in the accompanying figures. These figures are intended to be illustrative, not limiting. Although the disclosure is generally described in the context of these embodiments, it should be understood that it is not intended to limit the scope of the disclosure to these particular embodiments. Items in the figures may not be to scale.
Figure (“FIG.”) 1 depicts an overall structure of a main framework for blind super-resolution for both training and inference, according to embodiments of the present disclosure.
In the following description, for purposes of explanation, specific details are set forth in order to provide an understanding of the disclosure. It will be apparent, however, to one skilled in the art that the disclosure can be practiced without these details. Furthermore, one skilled in the art will recognize that embodiments of the present disclosure, described below, may be implemented in a variety of ways, such as a process, an apparatus, a system, a device, or a method on a tangible computer-readable medium.
Components, or modules, shown in diagrams are illustrative of exemplary embodiments of the disclosure and are meant to avoid obscuring the disclosure. It shall be understood that throughout this discussion that components may be described as separate functional units, which may comprise sub-units, but those skilled in the art will recognize that various components, or portions thereof, may be divided into separate components or may be integrated together, including, for example, being in a single system or component. It should be noted that functions or operations discussed herein may be implemented as components. Components may be implemented in software, hardware, or a combination thereof.
Furthermore, connections between components or systems within the figures are not intended to be limited to direct connections. Rather, data between these components may be modified, re-formatted, or otherwise changed by intermediary components. Also, additional or fewer connections may be used. It shall also be noted that the terms “coupled,” “connected,” “communicatively coupled,” “interfacing,” “interface,” or any of their derivatives shall be understood to include direct connections, indirect connections through one or more intermediary devices, and wireless connections. It shall also be noted that any communication, such as a signal, response, reply, acknowledgement, message, query, etc., may comprise one or more exchanges of information.
Reference in the specification to “one or more embodiments,” “preferred embodiment,” “an embodiment,” “embodiments,” or the like means that a particular feature, structure, characteristic, or function described in connection with the embodiment is included in at least one embodiment of the disclosure and may be in more than one embodiment. Also, the appearances of the above-noted phrases in various places in the specification are not necessarily all referring to the same embodiment or embodiments.
The use of certain terms in various places in the specification is for illustration and should not be construed as limiting. A service, function, or resource is not limited to a single service, function, or resource; usage of these terms may refer to a grouping of related services, functions, or resources, which may be distributed or aggregated. The terms “include,” “including,” “comprise,” “comprising,” or any of their variants shall be understood to be open terms, and any lists of items that follow are example items and not meant to be limited to the listed items. A “layer” may comprise one or more operations. The words “optimal,” “optimize,” “optimization,” and the like refer to an improvement of an outcome or a process and do not require that the specified outcome or process has achieved an “optimal” or peak state. The use of memory, database, information base, data store, tables, hardware, cache, and the like may be used herein to refer to system component or components into which information may be entered or otherwise recorded.
In one or more embodiments, a stop condition may include: (1) a set number of iterations have been performed; (2) an amount of processing time has been reached; (3) convergence (e.g., the difference between consecutive iterations is less than a first threshold value); (4) divergence (e.g., the performance deteriorates); (5) an acceptable outcome has been reached; and (6) all of the data has been processed.
One skilled in the art shall recognize that: (1) certain steps may optionally be performed; (2) steps may not be limited to the specific order set forth herein; (3) certain steps may be performed in different orders; and (4) certain steps may be done concurrently.
Any headings used herein are for organizational purposes only and shall not be used to limit the scope of the description or the claims. Each reference/document mentioned in this patent document is incorporated by reference herein in its entirety.
It shall be noted that any experiments and results provided herein are provided by way of illustration and were performed under specific conditions using a specific embodiment or embodiments; accordingly, neither these experiments nor their results shall be used to limit the scope of the disclosure of the current patent document.
It shall also be noted that although embodiments described herein may be within the context of blind image super-resolution, aspects of the present disclosure are not so limited. Accordingly, aspects of the present disclosure may be applied or adapted for use in other contexts.
Image super-resolution (SR) refers to the process to recover high-resolution (HR) images from low-resolution (LR) inputs. It is an important image processing technique to enhance image quality which subsequently helps to improve high-level computer vision tasks. Mathematically, the degradation process may be expressed as:
y=(x⊗k)↓s (1)
where x is the ground-truth (GT) HR image, y is the degraded LR image, and k is the blur kernel. The LR degradation process applies a two-dimensional convolution ⊗ first before resampling (↓s where s is the scaling factor) to only keep a single pixel for each distinct s×s patch. The SR process aims to recover x from y using a Restorer as follows:
{circumflex over (x)}=(y) (2)
Recently, various deep learning techniques have led to developments of many deep learning based SR models with remarkable results. These deep learning models rely on a large set of synthetic training HR-LR image pairs, where in each pair, the LR image is degraded from the HR image using a fixed and ideal blur kernel k* (usually a Bicubic downscaling kernel with antialiasing using MATLAB's default imresize command). Although they work very well on benchmark dataset with the same blur kernel k* as in training, their performances may drop significantly in real-world LR images because the blur kernel deviates from this ideal situation.
To solve this challenge, various methods have been proposed to train SR models which are applicable to multiple blur kernels where the ground truth (GT) blur kernel is used as an input of the model. However, in blind SR problem where the GT blur kernel is unknown, an additional step may be needed to estimate the blur kernel from given LR image. This two-step process adds a separate Estimator before the Restorer in Equation 2:
{circumflex over (x)}=(y,{circumflex over (k)})=(y,(y)) (3)
This two-step process is often time-consuming and the estimation error in could accumulate and lead to significant performance drop in . To solve this efficiency and accuracy challenge, more recent models try to train both Estimator and the Restorer in the following iterative process:
{circumflex over (x)}
i=(y, {circumflex over (k)}i-1)
{circumflex over (k)}
i=(y,{circumflex over (x)}i) (4)
In some previous models, kernel features using principal component analysis (PCA) transformation is used in place of kernel k itself. While the PCA transformation helps reduce kernel feature dimensionality and remove unnecessary kernel variations from the Restorer training, this linear transformation may not be optimal in representation of kernel features. In the present patent document, inspired by works using variational autoencoder (VAE) that projects inputs into a normal distribution of features in latent space, embodiments of a variational kernel autoencoder (VKAE) are disclosed in the present patent document. The VKAE may be pre-trained to optimize a kernel encoder F that reduces dimensionality and a kernel decoder G that reconstructs the blur kernel from compressed kernel features. The kernel encoder may be used to guide the kernel Estimator to learn a more effective degradation representation efficiently. With the kernel decoder, embodiments of a novel kernel-agnostic loss are presented to help the kernel Estimator to learn more robust degradation representations. In addition, embodiments of an attention-based adaptive pooling are further presented with attention to the spatial non-uniformity of the kernel features in the latent space. Comprehensive experiments on some typical benchmark datasets validate the new state-of-the-art (SOTA) of the disclosed scheme.
Contributions of the present patent disclosure include at least the following: (1) VAE is innovatively leveraged to learn kernel features in latent space which greatly improves the learning efficacy and efficiency of the kernel Estimator in iterative blind SR models; (2) Embodiments of a novel kernel-agnostic loss are disclosed to help the kernel Estimator learn robust kernel features from LR inputs without using GT kernel references; and (3) To overcome the spatial non-uniformity of estimated kernel features, embodiments of an attention-based adaptive pooling are disclosed to improve accuracy of the kernel Estimator, and hybrid kernel features that mix the spatially-invariant global features and the spatially-variant local features may be used to increase the kernel error tolerance of the SR Restorer.
To address the challenge of random unknown blur kernel in blind SR problems, one kind of model focuses on estimating the real blur kernel from given LR and combines it with existing non-blind SR models which take both an LR image and its corresponding blur kernel as inputs for SR restoration. Recently, the internal patch recurrence property in images is utilized to estimate the blur kernel using a kernel generator (KernelGAN). An additional SR restoration step is applied using the ZSSR (“Zero-Shot” Super-Resolution using Deep Internal Learning) model with the generated kernel. No additional HR-LR pairs are needed for training in both steps. More recently, a normalizing flow-based kernel prior (FKP) is used to model blur kernels with greater success. It is shown to be able to either estimate the blur kernel using an LR discriminator like in KernelGAN or estimate both the SR image and blur kernel combining FKP and Deep Image Prior (DIP). Most recently, some use a non-blind SR model, deep Unfolding Super-Resolution Network (USRNet), to generate the final SR using estimated kernel from FKP. These two-step processes are time consuming as both the kernel estimation and SR restoration processes are iterative.
Another line of models tries to solve the blind SR problem as a one-step process by embedding the kernel or kernel-feature estimation module either explicitly or implicitly. A kernel estimator is trained to generate blur kernel used for initial SR restoration. The intermediate SR result is further fed into a correction network to iteratively correct the estimated kernel. Some replace the initial kernel estimator and subsequent correction module with one estimator which makes the whole network trainable in an end-to-end fashion. Both models use iterative kernel estimation from LR and intermediate SR, and use PCA transformed kernel features in place of the full kernel. Others use a degradation encoder to embed kernel features in latent space without explicit kernel representation. Yet others use a downsampling network to predict spatially-variant kernels, which are fed into kernel-oriented adaptive local adjustment (KOALA) modules to modulate the features in the upsampling network, which generates an SR result with spatially-variant upsampling kernels.
While the bicubic interpolation is most commonly used for standard non-blind SR models, there is no universal representation for blur kernels for blind SR methods. First, while some models deal with isotropic Gaussian kernels only and some work with anisotropic Gaussian only, many others work for both isotropic and anisotropic kernels but are trained and evaluated separately. Another key difference lies in the definition of blur kernel in regards to the full resize process. In some recent work, as shown in Equation 1, the SR blur kernel is the only convolutional kernel applied in the full process. Alternatively, many models apply the SR blur kernel on HR images before resizing using additional bicubic interpolation. In another recent work, the model is trained with synthesized data from the second definition while only tested using the DIV2K random kernel (DIV2KRK) dataset which is generated using the first option. Although these two options may generate LR images that are equivalent, the latter case is less inclusive since it has a minimum kernel width as contributed by bicubic interpolation. In the case of DIV2KRK, random multiplicative noise is also applied on top of anisotropic Gaussian kernels before normalized back to the sum of 1.
Autoencoder (AE) is an unsupervised artificial neural network that compresses the data to lower dimension and then reconstructs the input back. It's based on an encoder-decoder architecture, where the encoder encodes the high-dimensional data to lower-dimension and the decoder takes the lower-dimensional data and tries to reconstruct the original high-dimensional data. Comparing to PCA, while maintaining the ability to find the representation of important data features data by removing noise and redundancies, AE is a more sophisticated and complex technique that can model relatively complex relationships and non-linearities. As the autoencoder is trained with reconstruction loss from the decoder output only, the latent space where the encoded vectors lie, may not be continuous. In other words, the distance or error in latent space is not necessarily translated to error in outputs. There could be gaps in latent space due to sparse distribution of features, where feature samples from these gaps would lead to meaningless reconstruction by the decoder. This sparse distribution is not optimal to guide the learning of one or more embodiments of the kernel Estimator. Variational autoencoder (VAE) converts the input dataset to a standard normal distribution in latent space which is dense and continuous. It has been extensively applied in various computer vision models, especially generative networks. However, it appears that VAE has never been used to model blur kernel in SR models.
Referring back to
In one or more embodiments, the full iterative process for SR model training may be described as:
z=(k)
{circumflex over (x)}
i=(y,{circumflex over (z)}i-1),{circumflex over (z)}i=(y,{circumflex over (x)}i)i=1, . . . n
{circumflex over (k)}=(y,{circumflex over (z)}n) (5)
where z is the kernel feature vector 142 encoded, by the kernel encoder 152, from the GT kernel k 162, {circumflex over (x)}i is the intermediate SR from the Restorer at the ith iteration, {circumflex over (z)}i is the intermediate kernel feature vector by the Estimator at the ith iteration, and {circumflex over (k)} is the estimated kernel 160 decoded from the estimated kernel feature vector {circumflex over (z)}n 140 by the kernel decoder . The main restoration loss Lrst may be calculated as the L1 loss between {circumflex over (x)}n and x.
Given that the blind SR model is used for image SR for blind images without GT kernels, it is desirable and advantageous to train blind SR model with LR images without GT kernels. Such training would increase the robustness of the blind SR model. In one or more embodiments, the training set may comprise multiple blind LR images without GT kernels (or even corresponding HR images). Section C.3 describes details using these blind LR images to introduce a kernel-agnostic loss Lkag to train the blind SR model with the VKAE. The training process using the kernel-agnostic loss Lkag may be implemented concurrently or separately from the training process using the HR-LR image pairs with GT kernels.
As shown in
As detailed in section C.3, the kernel decoder may be used to introduce a novel kernel-agnostic loss Lkag to increase the robustness of kernel Estimator. In one or more embodiments, the added kernel encoder and decoder modules may be used only in the training process to minimize Lkfeat and Lkag, but it may not be used at inference, so the model efficiency is not impacted by the complexity of the kernel encoder and decoder.
In one or more embodiments, leaky rectified linear unit (ReLU) may be used in the encoder while ReLU may be utilized in the decoder with exception of the last layer where a Sigmoid function may be applied. The pre-trained VKAE converts random kernel inputs to a standard normal distribution of features in a latent space. This dense distribution assures a randomly sampled feature point in the latent space corresponds to a realistic kernel as generated by the decoder. Thus the full blind SR model is more robust with higher tolerance of the kernel estimation error.
Previous blind image SR models, which train kernel estimation and image restoration modules together, have always included a kernel or kernel feature loss that calculates the distance between estimated features and GT kernel references. Such blind image SR models require a fully labeled image set that includes LR-HR pairs and GT kernels. In order to learn more robust kernel features, innovative embodiments of a kernel-agnostic loss (KAL) are disclosed in the present patent document to learn from LR images without using the GT reference kernels. In one or more embodiments, the KAL may be designed by taking advantage of the following kernel-revertible property.
As explained in Section A, convolution with the blur kernel is the first step to model degradation from HR to LR images. Without resampling, this degradation process is simply b=x⊗k. Given a 2D transformation , it could be proven that the following equation is valid, when is either a 90° rotation or a transposition:
(b)=(x)⊗(k) (6)
Subsequently, this theorem is still valid when the transformation is a sequence of one or more such rotations and transpositions. The proof of these theorems are described in subsection 3a), below. It may be seen from Equation (6) that the SR kernel of (b) is (k). Therefore, a reversed transformation −1 to revert (k) may be applied to the original kernel k. For example, for a transposition of a kernel, its reversed transformation is also a transposition; while for a 90° rotation, its reversed one is a 90° rotation in the opposite direction. In other words, the blur kernel of a transformed image is revertible when the transformation is limited to the aforementioned variations. Accordingly, a kernel-agnostic loss is designed to improve the robustness of the disclosed kernel estimation method.
In one or more embodiments, the similarity of two kernels may be defined as:
where W is the kernel size and (i, j) correspond to different elements of the kernel matrix.
For a pair of LR image y and y′ where y′=(y), a more accurate kernel estimation module may increase the similarity between {circumflex over (k)} and −1({circumflex over (k)}′) based on the kernel-revertible property. Here {circumflex over (k)} is the estimated kernel of y, and {circumflex over (k)}′ is the estimated kernel of y′. However, using the negative value of this similarity as training loss directly could lead the kernel Estimator to a trivial solution of estimating a certain isotropic kernel in regardless of inputs, which makes {circumflex over (k)} and −1({circumflex over (k)}′) identical and results in a loss of zero. In one or more embodiments, to prevent this, a kernel-agnostic loss may be used as follows:
where the temperature t controls the strength of penalties caused by the similarity between {circumflex over (k)} and (k).
This kernel-agnostic loss may be considered to resemble the contrastive loss that is used in unsupervised and semi-supervised learning. In the case of contrastive loss, it tries to increase the contrast in similarity between positive pairs or groups as sampled from intra-class samples, and negative ones from inter-class samples. For the kernel-agnostic loss, it forms positive pairs {circumflex over (k)} and −1(k′) as supported by the kernel-revertible property, and negative pairs {circumflex over (k)} and ({circumflex over (k)}) caused by kernel asymmetry property from the anisotropic assumption. This loss enables training the full model end-to-end using LR-HR pairs with unknown kernels (hence kernel-agnostic). It makes the kernel Estimator more robust in real-world applications where there is no GT kernel available for the input LR images.
The kernel decoder decodes (610) the first estimated kernel feature vector {circumflex over (z)}n and the second estimated kernel feature vector {circumflex over (z)}n′ into a first estimated kernel {circumflex over (k)} and a second estimated kernel {circumflex over (k)}′ respectively.
An inversely transformed kernel (k′) is obtained (615) by applying an inverse transformation of the 2D transformation to the second estimated kernel {circumflex over (k)}′. Afterwards, a similarity between the first estimated kernel {circumflex over (k)} and the inversely transformed kernel −1({circumflex over (k)}′) is obtained (620). In one or more embodiments, the similarity may be defined as shown in Equation (7). In a theoretical situation, the first estimated kernel feature vector k and the transformed kernel feature vector −1({circumflex over (k)}′) should be identical as indicated by the aforementioned theorem (with proven details in subsection a) below), and thus the similarity shall be 1. However, in real applications, the first estimated kernel feature vector {circumflex over (k)} and the inversely transformed kernel feature vector −1({circumflex over (k)}′) may not be the same, and the difference, as indicated by a similarity not equal to 1, may be used for blind SR model training.
A kernel-agnostic loss Lkag is constructed (625) to comprise the similarity. In one or more embodiments, the kernel-agnostic loss Lkag further incorporates a second similarity sim({circumflex over (k)}, ({circumflex over (k)})), which is between the first estimated kernel {circumflex over (k)} and a transformed kernel ({circumflex over (k)}) transformed from the first estimated kernel {circumflex over (k)} via the 2D transformation. The second similarity is used as a penalty to prevent the blind SR model from figuring out a trivial solution to estimate the same isotropic kernel for both the transformed LR image and the original LR image. In one or more embodiments, the kernel-agnostic loss Lkag incorporates a parameter τ to control the strength of penalties caused by the similarity between {circumflex over (k)} and ({circumflex over (k)}).
The blind SR model is trained (630) using at least the kernel-agnostic loss Lkag. In one or more embodiments, the training process using the kernel-agnostic loss Lkag may be implemented together the restoration loss Lrst and the kernel feature loss Lkfeat, with the three different losses having the same or different weights in an overall loss during training.
Described in this subsection is detailed proof for theorems of kernel-revertible property. As claimed in Section C.3, if the following is known
b=⊗k
where b, x, and k are all 2D discrete signals, then the following relationship still holds to be true as represents a series of 90° rotation or transposition applied to the 2D discrete signal.
Theorem 1: For a 2D discrete signal x(n) where n=[n1, n2]T are the coordinates of the discrete signal, and its corresponding 2D discrete Fourier transformation (x(n))=X(ω) where ω=[ω1, ω2]T, the following must be true:
(x(Rn))=X(Rω)
if RT=R−1. Here R is a 2×2 transformation matrix that is multiplied with 2D coordinates n or ω.
From 2D discrete Fourier transform (DFT), the following is known:
This is equivalent to (x(n))=X(ω)=Σx(n)e−j2πω
Applying R to n and denoting it as n′=Rn, one may obtain:
n=R
−1
n′=R
T
n′
Applying Fourier transform to the new signal x(n′), one may get:
(x(n′))=Σx(n′)e−j2πω
Plugging in n=RTn′, the following equation may be obtained:
(x(n′)=Σx(n′)e−j2πω
And thus (x(Rn))=X(Rω).
Theorem 2: As long as represents a series of 90° rotation or transposition applied to a 2D discrete signal x(n), (x) is equivalent to x(Rn) where the 2D matrix R satisfies RT=R−1
In linear algebra, the rotation matrix of any angle θ is:
It is trivial to see RT=R−1 here and a 90° rotation is a special case of either θ=90° or θ=270°. For transposition, it is simply the switch of coordinate order and the transformation matrix may be written as:
And again RT=R−1.
It can be shown that if R=Πi=1n, Ri and RiT=Ri−1 for any i, then RT=R−1 since
Consequently, if represents a series of 90° rotation or transposition, (x) is equivalent to x(Rn) where R=Πi=1nRi corresponds to the series of transformations. Since each Ri is either 90° rotation or transposition and it is shown above that both satisfy RiT=Ri−1, one can conclude that the above R satisfies RT=R−1.
If the Fourier transformation of b, x, and k are denoted as B(ω), X(ω), and K(ω) respectively, then B(ω)=X(ω)K(ω).
Thus for any transformation matrix R, one may have B(Rω)=X(Rω)K(Rω).
Assuming R is the transformation matrix corresponding to , one may get (b(Rn))=(x(Rn))(k(Rn)) based on Theorem 1 since RT=R−1.
And supported by Theorem 2, the equation ((b))=((x))((k)) is obtained.
Thus, the equation (6) (b)=(x)⊗(k) is proved.
Motivated by the fact that the accuracy in kernel estimation may not be spatially uniform, embodiments of a novel attention based adaptive pooling and hybrid kernel feature approach are disclosed in this section. In general, patches with highly focused foreground structures are easier to recover their GT kernels comparing to patches with out-of-focus backgrounds or minimum color variations. For example, for a HR patch of uniform color, the degraded LR is the same regardless of the blur kernel which makes the kernel estimation impossible.
This spatially non-uniform property makes the previously widely used global average pooling (GAP) in the kernel Estimator sub-optimal as it does not differentiate areas with variant kernel estimation confidences. To solve this problem, embodiments of an attention-based adaptive pooling are disclosed in the presented blind SR model which assigns different weights to pixels of the patch.
A default GAP process may be denoted as:
In one or more embodiments, the attention process may be defined as a=(f′), where f′ is the feature input to the final convolution layer 705 in the kernel Estimator, and the kernel feature {circumflex over (z)}α with attention based adaptive pooling may be defined as:
The GAP may be viewed as a special case of this adaptive pooling where the attention for each pixel is the same (i.e., 1). For general cases, pixels with higher confidence in kernel estimation tend to have higher attention, as evidenced by visual examples in
In one or more embodiments, instead of the spatially-invariant global feature {circumflex over (z)}, a hybrid kernel feature {circumflex over (z)}h=α{circumflex over (z)}+(1−α)f may be used to concatenate with LR features in the SR Restorer. It is a simple linear mixture between {circumflex over (z)} and spatially-variant local feature f, where α is a learnable parameter which may vary at different layers. This hybrid feature adds minimum number of model parameters and does not change model complexity of the Restorer, while enabling the Restorer to learn from local kernel features for additional tolerance of kernel estimation uncertainty. In one or more embodiments, the spatially-invariant global feature {circumflex over (z)} may be the kernel feature {circumflex over (z)}α with attention-based adaptive pooling, such that both attention-based adaptive pooling and hybrid kernel feature may be applied together in blind SR training and operation.
It shall be noted that these experiments and results are provided by way of illustration and were performed under specific conditions using a specific embodiment or embodiments; accordingly, neither these experiments nor their results shall be used to limit the scope of the disclosure of the current patent document.
A total of 3450 HR images from various data sets are used to synthesize degraded LR images with random kernels for training. The degradation setting is similar to the one used in KernelGAN. It generates more general and irregular blur kernels which are modeled as anisotropic Gaussian kernels with multiplicative noise. During training, the kernel width λ is uniformly sampled from [0.6, 5.0] and the multiplicative noise is uniform sampled from [0.75, 1.25]. The kernel size is set as 15×15 to fit the kernel autoencoder design. Same as KernelGAN, the kernels used for ×2 are also applied to ×4 models, where the kernel convolution and ×2 resampling are applied twice to resize the HR image to LR.
For quantitative evaluation, HR images from various datasets are used. The LR images are synthesized from HR images following the same degradation settings used for training. Due to the large variations in random degradation, three sets of LR images are generated for each test set and the results described in the following experiments are the average of all three sets. An image set comprising LR images with unknown kernels is also included for direct comparison with other models which have been tested using the same image set. As some existing blind SR models were trained using different degradation settings, two SOTA models, deep alternating network (DAN) and degradation-aware super-resolution (DASR), are also retrained using presenting degradation settings for fair comparison as included in Table 1. They are denoted as DAN* and DASR* respectively.
36.90/0.9504
32.24/0.8894
30.97/0.8661
29.71/0.8917
34.35/0.9236
34.28/0.9252
36.96/0.9518
32.59/0.8956
31.26/0.8739
30.40/0.9038
34.73/0.9288
34.31/0.9258
30.05/0.8482
27.27/0.7268
26.79/0.6921
24.77/0.7276
29.30/0.8009
29.29/0.8024
30.42/0.8555
27.60/0.7363
26.94/0.6998
25.00/0.7381
29.50/0.8068
29.30/0.8026
The LR patches used for training are 64×64 in dimension for all scaling factors. Each model is trained for 125 epochs, where each epoch includes 5000 iterations. Using Adam optimizer, the model is trained with an initial learning rate of 1×10−4 which decays by half after every 25 epochs. All training is conducted on 2 to 4 NVIDIA V100 GPU cards. For training of the kernel autoencoder, the same degradation settings are used to synthesize random kernels. The autoencoder is trained 400 epochs, each has 10 iterations of a batch of 8000 random kernels. The Adam optimizer starts from a learning rate of 1×10−3 using cosine annealing schedule.
To demonstrate the effectiveness of the proposed variational kernel autoencoder, an experiment is conducted to assess its performance with varying number of iterations at inference. It is compared with DAN using the average of three Set14 test sets. For fair comparison, the DAN model is re-trained using the same data degradation settings as the presented VKAE, and the same Estimator and Restorer as DAN are used while PCA is replaced with the presented VKAE. As shown in the left chart of
As shown in Table 1, experiments were conducted on ×2 and ×4 scaling factors following dataset 6. For each scaling factor, the compared models are categorized as four groups. The first group comprises model and methods, including Bicubic, Bicubic+ZSSR, and residual channel attention networks (RCAN), assuming standard bicubic interpolation in degradation. The results of these models are worse in general as expected. The second group includes SOTA models like Iterative Kernel Correction (IKC), DAN, DASR, and a blind SR framework based on kernel-oriented adaptive local adjustment (KOALANet) whose trained-model are available. These models are trained with degradation settings (Gaussian blur before additional bicubic interpolation) different from dataset 6 and embodiments of the presented model. Additionally, only isotropic Gaussian kernels were used for training in IKC, and DASR in scaling factor of ×2. Even with discrepancies in degradation settings, these models outperform methods in the first group which are only optimized for bicubic interpolation.
The third group includes two-step methods, KernelGAN+ZSSR and FKP+USRNet, which apply a blur kernel estimation model and a non-blind SR model separately. These methods use similar degradation settings as the presented model so the direct comparison is reasonable. However, these two-step methods fall behind those in the previous group significantly in terms of accuracy. This again shows the non-blind SR model is very sensitive to errors introduced in the kernel estimation model. In the case of FKP+USRNet process, its performance suffers greatly comparing to the test results in FKP for similar test datasets. The main cause is the increased kernel variation in the test sets where random multiplicative noise is applied on top of anisotropic Gaussian blur.
For fair comparison, two of the best models in the second group, DAN and DASR, are trained using the same degradation settings as the presented model, denoted as DAN* and DASR* respectively. As shown in Table 1, with great improvements in kernel feature representation in latent space, the VKAE model embodiment achieves the best in all test sets in both ×2 and ×4 scaling factors. For dataset 6 that has been assessed in various studies, the VKAE model is able to raise the SOTA results in PSNR from 32.56 dB and 28.15 dB to 34.31 dB and 29.30 dB for ×2 and ×4 scaling factors respectively. Embodiments of the VKAE model show obvious improvements in restoring sharp edges and high frequency details even comparing to the next best DAN* model.
To demonstrate the effectiveness of the proposed variational kernel autoencoder (VKAE), an ablation study was conducted to compare a baseline model using standard autoencoder (AE) with accumulatively added innovative modules, including VKAE, hybrid kernel feature α{circumflex over (z)}+(1−α)f, attention-based adaptive pooling and kernel-agnostic loss Lkag. Three datasets with ×2 scaling factor are used for assessment. As shown in Table 2, these innovated modules are able to improve accuracy step by step consistently for all three datasets. There is one exception of Dataset 4 as highlighted in bold font, where the restoration accuracy in both PSNR and SSIM drops when the non-uniform kernel feature is introduced. This may be caused by the bias in Dataset 4 which includes structures with ubiquitous line patterns.
30.18/0.8994
Embodiments of the present VKAE method are also compared with others on old historic images which have no ground-truth references. Qualitative results of one ×4 example are shown in
6. Attention based Adaptive Pooling
To have a better understanding of the disclosed attention-based adaptive pooling in Section C.4, an example of the related attention map is visualized in
Visual examples of experiments in Section D.2 shows the effects of more accurate kernel estimation with increasing number of iterations, which result in better restoration quality step by step. It also demonstrates the advantage of the presented blind SR model comparing to PCA, generating high fidelity restoration with less iteration needed. Visual comparison among recovered images using different models also verifies that embodiments of the presented model may have more power to restore a more accurate and sharper image in general.
Blind image SR tends to be a challenging problem due to the difficulty in finding an effective kernel representation that is optimal for the full restoration process. To attack this challenge, novel blind SR method embodiments based on VAE with kernel agnostic loss are disclosed in the present patent document. The non-linearity feature representation ability of VAE leads to more effective estimation of the kernel, and its dense feature distribution in latent space results in a more efficient kernel estimation in the iterative process. While attention-based adaptive pooling leads to a more accurate kernel feature estimation, both the kernel agnostic loss and hybrid kernel feature make the model more robust to uncertainties in kernel feature estimation. Comprehensive experiments on both synthetic and real image data sets validate a remarkably better performance of embodiments of the disclosed model over the current SOTA methods.
In one or more embodiments, aspects of the present patent document may be directed to, may include, or may be implemented on one or more information handling systems (or computing systems). An information handling system/computing system may include any instrumentality or aggregate of instrumentalities operable to compute, calculate, determine, classify, process, transmit, receive, retrieve, originate, route, switch, store, display, communicate, manifest, detect, record, reproduce, handle, or utilize any form of information, intelligence, or data. For example, a computing system may be or may include a personal computer (e.g., laptop), tablet computer, mobile device (e.g., personal digital assistant (PDA), smart phone, phablet, tablet, etc.), smart watch, server (e.g., blade server or rack server), a network storage device, camera, or any other suitable device and may vary in size, shape, performance, functionality, and price. The computing system may include random access memory (RAM), one or more processing resources such as a central processing unit (CPU) or hardware or software control logic, read only memory (ROM), and/or other types of memory. Additional components of the computing system may include one or more drives (e.g., hard disk drive, solid state drive, or both), one or more network ports for communicating with external devices as well as various input and output (I/O) devices, such as a keyboard, mouse, touchscreen, stylus, microphone, camera, trackpad, display, etc. The computing system may also include one or more buses operable to transmit communications between the various hardware components.
As illustrated in
A number of controllers and peripheral devices may also be provided, as shown in
In the illustrated system, all major system components may connect to a bus 1216, which may represent more than one physical bus. However, various system components may or may not be in physical proximity to one another. For example, input data and/or output data may be remotely transmitted from one physical location to another. In addition, programs that implement various aspects of the disclosure may be accessed from a remote location (e.g., a server) over a network. Such data and/or programs may be conveyed through any of a variety of machine-readable medium including, for example: magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as compact discs (CDs) and holographic devices; magneto-optical media; and hardware devices that are specially configured to store or to store and execute program code, such as application specific integrated circuits (ASICs), programmable logic devices (PLDs), flash memory devices, other non-volatile memory (NVM) devices (such as 3D XPoint-based devices), and ROM and RAM devices.
Aspects of the present disclosure may be encoded upon one or more non-transitory computer-readable media with instructions for one or more processors or processing units to cause steps to be performed. It shall be noted that the one or more non-transitory computer-readable media shall include volatile and/or non-volatile memory. It shall be noted that alternative implementations are possible, including a hardware implementation or a software/hardware implementation. Hardware-implemented functions may be realized using ASIC(s), programmable arrays, digital signal processing circuitry, or the like. Accordingly, the “means” terms in any claims are intended to cover both software and hardware implementations. Similarly, the term “computer-readable medium or media” as used herein includes software and/or hardware having a program of instructions embodied thereon, or a combination thereof. With these implementation alternatives in mind, it is to be understood that the figures and accompanying description provide the functional information one skilled in the art would require to write program code (i.e., software) and/or to fabricate circuits (i.e., hardware) to perform the processing required.
It shall be noted that embodiments of the present disclosure may further relate to computer products with a non-transitory, tangible computer-readable medium that have computer code thereon for performing various computer-implemented operations. The media and computer code may be those specially designed and constructed for the purposes of the present disclosure, or they may be of the kind known or available to those having skill in the relevant arts. Examples of tangible computer-readable media include, for example: magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CDs and holographic devices; magneto-optical media; and hardware devices that are specially configured to store or to store and execute program code, such as ASICs, PLDs, flash memory devices, other non-volatile memory devices (such as 3D XPoint-based devices), and ROM and RAM devices. Examples of computer code include machine code, such as produced by a compiler, and files containing higher level code that are executed by a computer using an interpreter. Embodiments of the present disclosure may be implemented in whole or in part as machine-executable instructions that may be in program modules that are executed by a processing device. Examples of program modules include libraries, programs, routines, objects, components, and data structures. In distributed computing environments, program modules may be physically located in settings that are local, remote, or both.
One skilled in the art will recognize no computing system or programming language is critical to the practice of the present disclosure. One skilled in the art will also recognize that a number of the elements described above may be physically and/or functionally separated into modules and/or sub-modules or combined together.
It will be appreciated to those skilled in the art that the preceding examples and embodiments are exemplary and not limiting to the scope of the present disclosure. It is intended that all permutations, enhancements, equivalents, combinations, and improvements thereto that are apparent to those skilled in the art upon a reading of the specification and a study of the drawings are included within the true spirit and scope of the present disclosure. It shall also be noted that elements of any claims may be arranged differently including having multiple dependencies, configurations, and combinations.
Filing Document | Filing Date | Country | Kind |
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PCT/CN2021/122050 | 9/30/2021 | WO |