1. Field of the Invention
The present invention relates to an image processing apparatus for performing an image adjusting process, image processing method, program and storage medium thereof.
2. Description of the Related Art
Conventionally, an image processing apparatus performing an image adjusting process is known. For example, a technique that separates a scanned image into two areas, that is, text and halftone areas, applies an edge emphasis process to the text area, and applies a smoothing process to the halftone area so as to enhance the sharpness and to reduce moiré at the same time is available (see Japanese Patent Laid-Open No. 2002-077623).
However, since an adjusting process to be applied to an image generally depends on the attributes of that image, if the attribute of the image is determined erroneously, an appropriate adjusting process can no longer be applied. For example, if a smoothing process is applied to the text area or an edge emphasis process is applied to the halftone area, the image deteriorates. For example, if one part of a character is determined as a text area, and the remaining part of that character is determined as a halftone area, switching of the edge emphasis and smoothing processes occurs during one character, thus considerably deteriorating the image. In particular, conventionally, since the ON/OFF states of the adjusting processes such as the edge emphasis process and the like are switched according to the attributes of an image, no transition state exists. For example, an image deteriorates at the position where the processes are switched.
The present invention allows realization of an improvement in image quality by performing an image adjusting process appropriately.
According to one aspect of the present invention, the foregoing problem is solved by providing an image processing apparatus, which applies an adjusting process to an image that includes a pixel to be processed, the apparatus comprising an extraction unit adapted to extract an image area with a predetermined size including the pixel to be processed, a variation calculation unit adapted to calculate a variation with respect to the pixel to be processed from signal values of pixels included in the image area, a variation time count calculation unit adapted to calculate a variation time count with respect to the pixel to be processed from the signal values of the pixels included in the image area, a definition unit adapted to define correspondence among the variation time count, the variation, and an adjusting level and an adjusting unit adapted to calculate the adjusting level from the variation time count and the variation using the definition unit, and to apply an adjusting process to a signal value of the pixel to be processed by the calculated adjusting level, wherein the definition unit defines the correspondence so that the adjusting level progressively changes in accordance with different variation time counts or different variations.
An image processing method, which applies an adjusting process to an image that includes a pixel to be processed, comprising the steps of extracting an image area with a predetermined size including the pixel to be processed, calculating a variation with respect to the pixel to be processed from signal values of pixels included in the image area, calculating a variation time count with respect to the pixel to be processed from the signal values of the pixels included in the image area and calculating an adjusting level from the variation time count and the variation using a definition unit which defines correspondence among the variation time count, the variation, and the adjusting level, and applying an adjusting process to a signal value of the pixel to be processed by the calculated adjusting level, wherein the definition unit defines the correspondence so that the adjusting level progressively changes in accordance with different variation time counts or different variations.
Further features of the present invention will become apparent from the following description of exemplary embodiments with reference to the attached drawings.
Preferred embodiments of the present invention will now be described in detail with reference to the drawings. It should be noted that the relative arrangement of the components, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
<Arrangement of MFP>
This MFP 1 basically has a function of printing data received from a host computer (PC) as a printer, and a function as a scanner. Furthermore, functions of the MFP alone include a copy function of printing an image scanned by the scanner using the printer, a function of printing image data read from a storage medium such as a memory card or the like, and a function of printing image data received from a digital camera or the like.
Referring to
The card interface 22 loads image data, which is captured by, for example, a digital still camera (to be abbreviated as DSC hereinafter) and is recorded on a memory card or the like, in accordance with operations at the operation unit 15. Note that the color space of the image data loaded via the card interface 22 is converted from that of the DSC (e.g., YCbCr) into a standard RGB color space (e.g., NTSC-RGB or sRGB) if necessary. The loaded image data undergoes various kinds of processes required for an application such as resolution conversion to the effective number of pixels, and the like, based on its header information. The camera interface 23 is used to directly connect the DSC and to read image data.
An image processor 12 executes image processes such as conversion of a read signal value, an image adjusting/modification process, conversion from luminance signals (RGB) into density signals (CMYK), scaling, gamma conversion, error diffusion, and the like. The adjusting process to be executed by the image processor 12 includes an edge emphasis process, smoothing process, substitution process, achromatization process, and the like, and the image processor 12 serves as an adjusting unit. Data obtained by the image processes in the image processor 12 is stored in a RAM 17. When adjusted data stored in the RAM 17 reaches a predetermined amount, the print unit 13 executes a print operation.
A nonvolatile RAM 18 comprises, for example, a battery backed-up SRAM or the like, and stores data unique to the MFP 1 or the like. The operation unit 15 comprises a photo direct print start key which allows the user to select image data stored in a storage medium and to start printing. The operation unit 15 also comprises a key used to print an order sheet, a key used to scan an order sheet, and the like. The operation unit 15 may also comprise a copy start key in a monochrome copy mode or color copy mode, a mode key used to designate a mode such as a copy resolution, image quality, and the like, a stop key used to stop a copy operation or the like, a numerical keypad used to input a copy count, a registration key, and the like. The CPU 11 detects the pressing states of these keys and controls respective units according to the detected states.
The display unit 19 comprises a dot matrix type liquid crystal display unit (LCD) and an LCD driver, and makes various displays under the control of the CPU 11. Also, the display unit 19 displays thumbnails of image data recorded in a storage medium. The print unit 13 comprises an ink-jet head of an ink jet system, general-purpose IC, and the like. The print unit 13 reads out print data stored in the RAM 17 and prints it out as a hard copy under the control of the CPU 11.
A drive unit 21 includes stepping motors for driving feed and discharge rollers, gears for transmitting the driving forces of the stepping motors, a driver circuit for controlling the stepping motors, and the like in order to operate the scanning unit 14 and print unit 15.
A sensor unit 20 includes a print sheet width sensor, print sheet sensor, document width sensor, document sensor, print medium sensor, and the like. The CPU 11 detects the states of a document and print sheet based on information obtained from this sensor unit 20.
A PC interface 24 is an interface with the PC, and the MFP 1 performs a print operation, scan operation, and the like from the PC via the PC interface 24. In a copy operation, image data scanned by the scanning unit 14 undergoes a data process inside the MFP, and is printed using the print unit 13.
Upon instruction of a copy operation from the operation unit 15, the scanning unit 14 scans a document set on the document table. The scanned data is sent to the image processor 12 and undergoes the image process. Then, the processed data is sent to the print unit 13, thus executing a print process.
<Image Process>
After that, the CPU 11 executes input device color conversion in STEP 302. As a result, the device-dependent color space of signal data is converted into a standard color space domain. For example, the standard color space includes sRGB specified by the IEC (International Electrotechnical Commission). Also, AdobeRGB propounded by Adobe Systems may be used. The conversion method includes an arithmetic method using a 3×3 or 3×9 matrix, a lookup table method which determines values based on a table that describes conversion rules, and the like.
In STEP 303, the CPU 11 applies an adjusting/modification process to the converted data. The process contents include an edge emphasis process that adjusts blurring due to scanning, a text modification process that improves legibility of text, a process for removing bleed-through that has occurred due to scanning upon light irradiation, and the like. In STEP 304, the CPU 11 executes an enlargement/reduction process to convert the data to a desired scale when the user designates a zoom scale, in a 2-in-1 copy mode that assigns two document images on one sheet, or the like. As the conversion method, methods such as bicubic, nearest neighbor, and the like are generally used.
In STEP 305, the CPU 11 converts the data on the standard color space into signal data unique to an output device. The MFP according to this embodiment adopts an ink-jet system, and executes a conversion process into ink color data such as cyan, magenta, yellow, black, and the like. This conversion can use the same method as in STEP 302.
Furthermore, in STEP 306 the CPU 11 converts the data into the number of printable levels. For example, in the case of binary expression, that is, ON/OFF of ink dots, the data may be binarized by a quantization method such as error diffusion or the like. As a result, the data is converted into a data format that the printer can print, and a print operation is executed based on that data, thus forming an image.
<Process Unit>
A case will be explained below wherein the process unit is an area unit. A 7×7 area is defined for a pixel indicated by “circle” in
However, since the pixel unit is preferably used as the process unit to define the adjusting level at higher accuracy, the pixel unit will be described as the process unit in this embodiment.
STEP 502 is a process area defining step. The process area is an area configured by a plurality of pixels (7×7 area in the above description) including the process unit, as described above.
STEP 503 is an adjusting level defining step. The CPU 11 sets an adjusting level for the process unit.
STEP 504 is an adjusting execution step. The CPU 11 adjusts the process unit using the adjusting level defined in STEP 503.
STEP 505 is a last adjusting target checking step. That is, the CPU 11 checks if the process unit is the last one. If the process unit is not the last one (NO), the process returns to STEP 501. If the process unit is the last one (YES), the process reaches “END”.
In the embodiments to be described hereinafter, the 7×7 area is used as the process area. This is because the pixel range of a document to be scanned by one pixel of the image sensing element (CCD or CIS) used in the scanning unit is designed to include six pixels or less. Although the pixel range to be scanned is designed to include six pixels or less, reflected light from a document that enters the image sensing element receives various influences due to float of a document from the document table, unevenness of a document, and the like. For this reason, one pixel of the image sensing element may scan a range exceeding six pixels. The embodiments to be described hereinafter show a plurality of graphs used to explain image signals obtained by scanning a document. However, these image signals are not always obtained from reflected light within six pixels or less.
<Definition of Terms>
Terms used in the present specification will be defined below.
A variation is a value that represents the magnitude of the variations of pixel signal values in a surrounding pixel group having a pixel to be processed as the center. In this embodiment, of the absolute values (edge amounts) of differences between luminance values of two pixels that neighbor one pixel on two sides, a maximum one will be explained as the variation. However, the present invention is not limited to such specific value. For example, the variation may be a value that expresses the difference (magnitude) of changes such as the absolute value of the primary derivative of a value related to an image signal of a pixel of interest or the like, or a value that representatively expresses the difference (magnitude) of changes of values related to image signals in an area of interest.
A variation time count is a value that represents the frequency of occurrence of variations of image signal values in a surrounding pixel group having the pixel to be processed as the center. In this embodiment, the difference between the luminance values of two pixels that neighbor one pixel on two sides in the image area is ternarized using −1, 0, and 1, and the frequency of occurrence of increment/decrement of ternary data (the number of sign changes (the number of zero-crossing points)) will be described as the variation time count. However, the present invention is not limited to such specific value. The variation time count is defined as a value that expresses the frequency of occurrence of changes in value associated with image signals such as the number of zero-crossing points or spatial frequency of the primary derivatives of values associated with image signals in the image area, a black-white change count after binarization, and the like.
A variation acceleration is a value that represents the acceleration of variations of pixel signal values in a surrounding pixel group having the pixel to be processed as the center. In the following embodiments, the variation acceleration will be explained as a value obtained by further calculating a difference from the differences of luminance values in the image area. However, the present invention is not limited to such specific value. For example, the acceleration variation may be a value that expresses the acceleration of changes such as the secondary derivative of values associated with image signals in an area of interest and the like.
A saturation will be explained as a maximum absolute value of image signal differences of respective colors of a pixel or area of interest in the following embodiments. However, the present invention is not limited to such specific value. The saturation is defined as a value that expresses the distance from the chromaticity axis.
Note that a range from 0 to 255 that an image signal can assume will be exemplified. However, the range of the image signal is not limited to such a specific one, and it may be defined to fit in the MFP or image process.
An adjusting level defining process will be described below. Note that a range from 0 to 255 that an image signal can assume will be exemplified. However, the range of the image signal is not limited to such a specific one, and it may be defined to fit in the MFP or image process.
This embodiment uses an edge level or magnitude as the adjusting level, and executes an edge emphasis filter process as the adjusting process. An edge emphasis amount of an edge emphasis filter (to be described later) is adjusted by the edge level, which is adaptively set based on the variation time count and variation. The edge level defining process and an application of the defined edge level will be described below.
<Adjusting Level Defining STEP 701: Define Process Area>
The CPU 11 defines a process area, that is, a 7×7 area including seven pixels in the horizontal direction and seven pixels in the vertical direction to have the pixel of interest as the center in an image configured by RGB multi-valued image signals, and generates a 7×7 process area of luminance L by calculating luminance L from respective pixel values of the process area by:
L=(R+2×G+B)/4 (1)
Note that this embodiment uses luminance L given by equation (1), but may adapt another luminance. For example, L* of a uniform color space L*a*b* may be used as a luminance, or Y of YCbCr may be used as a luminance.
<Adjusting Level Defining STEP 702: Extract Four Directions>
The CPU 11 extracts, from the process area of L generated in STEP 701, seven pixels in each of a total of four directions, that is, one horizontal direction, one vertical direction, and two oblique directions, as shown in
<Adjusting Level Defining STEP 703: Calculate L Difference>
The CPU 11 calculates differences Grd of L of five pixels in each direction from L in the four directions extracted in STEP 702, as shown in
Grd(i)=L(i+1)−L(i−1) (2)
where L(i−1) is a pixel before pixel L(i), and L(i+1) is a pixel after pixel L(i).
Note that the L difference calculation method is not limited to such a specific method. For example, differences between neighboring pixels may be calculated, or differences between pixels further separated from those before and after a given pixel described above may be calculated.
<Adjusting Level Defining STEP 704: Determine Edge Direction)
The CPU 11 calculates Grd absolute values in the four directions of the pixel of interest in Grd in the four directions calculated in STEP 703. The CPU 11 determines a direction that shows a maximum Grd absolute value of those in the four directions as an edge direction of the pixel of interest.
<Adjusting Level Defining STEP 705: Calculate Variation>
The CPU 11 can calculate five Grd values in STEP 703 from seven pixels that line up in the edge direction determined in STEP 704. The CPU 11 compares these five Grd values and calculates their maximum absolute value as a variation (edge amount) of the pixel of interest. An edge is stronger with increasing variation, and is close to flat with decreasing variation.
<Adjusting Level Defining STEP 706: Calculate Variation Time Count>
The CPU 11 calculates a variation time count as a total of the four directions from the Grd values in the four directions calculated in STEP 703. That is, the CPU 11 calculates, as the variation time count (the number of zero-crossing points) of the pixel of interest, the number of changes in the sign of Grd from + to − or vice versa, as shown in
Note that the first embodiment does not count, as the variation time count, a case in which the sign changes to sandwich zeros of a plurality of pixels, as shown in
As shown in
<Adjusting Level Defining STEP 707: Edge Level Defining Process 1 Based on Variation Time Count>
The CPU 11 adaptively defines an edge level or magnitude Fz1 in accordance with the variation time count calculated in STEP 706.
Fz1=(second threshold−variation time count)/(second threshold−first threshold) (3)
<Adjusting Level Defining STEP 708: Edge Level Defining Process 2 Based on Variation Time Count>
The CPU 11 adaptively defines an edge level Fz2 in accordance with the variation time count calculated in STEP 706.
Fz2=(variation time count−third threshold)/(fourth threshold−third threshold) (4)
Fz1×Fz2 can implement the edge level shown in
<Adjusting Level Defining STEP 709: Edge Level Defining Process Based on Variation>
The CPU 11 adaptively defines an edge level Fe in accordance with the variation calculated in STEP 705.
Fe=(variation−fifth threshold)/(sixth threshold−fifth threshold) (5)
<Adjusting Process STEP 1401: Calculate Edge Emphasis Amount>
The CPU 11 calculates differences (edge emphasis amounts) between the pixel value of interest upon applying an edge emphasis filter and that before application for respective colors in the 7×7 RGB areas defined in STEP 701. This embodiment will exemplify a case in which a 5×5 edge emphasis filter is applied to have the pixel of interest as the center. However, the filter size need only be smaller than the process area size defined in STEP 701, and filter coefficients may be appropriately defined.
ΔF=N1−N0 (6)
As shown in
<Adjusting Process STEP 1402: Adjust Edge Emphasis Amount by Fz1>
The CPU 11 adjusts the edge emphasis amounts ΔF calculated in STEP 1401 using the edge level Fz1 defined in STEP 707. The CPU H calculates an adjusted edge emphasis amount ΔFz1 using:
ΔFz1=Fz1×ΔF (7)
By the process in STEP 1402, a text area with a small variation time count can undergo relatively strong edge emphasis, and a halftone area with a large variation time count can undergo relatively weak edge emphasis. Hence, the sharpness of text can be enhanced, and moiré can be prevented from being emphasized at the same time.
<Adjusting Process STEP 1403: Adjust Edge Emphasis Amount by Fz2>
The CPU 11 adjusts the edge emphasis amount ΔFz1 calculated in STEP 1402 using the edge level Fz2 defined in STEP 708. The CPU 11 calculates an adjusted edge emphasis amount ΔFz2 using:
ΔFz2=Fz2×ΔFz1 (8)
When Fz2 is defined, as shown in
<Adjusting Process STEP 1404: Adjust Edge Emphasis Amount by Fe>
The CPU 11 adjusts the edge emphasis amount ΔFz2 calculated in STEP 1403 using the edge level Fe defined in STEP 709. The CPU 11 calculates an adjusted edge emphasis amount ΔFe using:
ΔFe=Fe×ΔFz2 (9)
By the process in STEP 1404, the edge area such as a character can undergo relatively strong edge emphasis, and the flat area such as a background or photo can undergo relatively weak edge emphasis. As a result, the sharpness of a character can be enhanced, moiré can be prevented from being emphasized, and a photo can be prevented from being roughened at the same time.
<Adjusting Process STEP 1405: Completion of Edge Emphasis Filter Process>
The CPU 11 calculates an edge emphasis filter process pixel value Ne by adding the edge emphasis amount ΔFe calculated in STEP 1404 to the pixel value N0 of interest, as given by:
Ne=N0+ΔFe (10)
Note that a process for clipping Ne within a desired range may be inserted.
Since the adjusting level can be changed according to not only the variation but also the variation time count, bad effects of moiré by edge emphasis to a halftone area can be eliminated. Furthermore, since the adjusting level can be adaptively defined according to the variation time count, bad effects of switching of processes due to the variation time count can be eliminated. Since the adjusting level can be adaptively defined according to the variation time count and variation, bad effects of switching of processes due to the variation time count and variation can be eliminated.
As shown in
The first embodiment has exemplified the case in which the edge emphasis process by means of the filter process is executed at the adaptive level. The second embodiment will exemplify a case in which edge emphasis and smoothing processes are executed at the adaptive level.
In STEP 707 of the first embodiment, an edge level Fz1 shown in
Another smoothing example will be described below.
<Adjusting Level Defining STEP 2010: Define Smoothing Level Based on Variation Time Count>
The CPU 11 adaptively defines a smoothing level Az in accordance with the variation time count calculated in STEP 706.
Az=(eighth threshold−variation time count)/(eighth threshold−seventh threshold) (11)
<Adjusting Process STEP 2206: Calculate Smoothing Amount>
The CPU 11 calculates change amounts (smoothing amounts) between the pixel value of interest upon applying a smoothing filter and that before application for respective colors in the 7×7 RGB areas defined in STEP 701. This embodiment will exemplify a case in which a 5×5 smoothing filter is applied to have the pixel of interest as the center. However, the filter size need only be smaller than the process area size defined in STEP 701, and filter coefficients may be appropriately defined.
ΔA=N2−N0 (12)
As shown in
<Adjusting Process STEP 2207: Adjust Smoothing Amount by Az>
The CPU 11 adjusts the smoothing amounts ΔA calculated in STEP 2206 using the smoothing level Az defined in STEP 2010. The CPU 11 calculates an adjusted smoothing amount ΔAz using:
ΔAz=Az×ΔA (13)
By the process in STEP 2207, the text area with a small variation time count undergoes relatively weak smoothing so as not to impair sharpness, and the halftone area with a large variation time count undergoes relatively strong smoothing so as to eliminate moiré.
<Adjusting Process STEP 2208: Completion of Smoothing Filter Process>
The CPU 11 calculates a filter-processed pixel value Nf by adding the smoothing amount ΔAz calculated in STEP 2207 to the edge emphasis filter-processed pixel value Ne, as given by:
Nf=Ne+ΔAz (14)
Note that a process for clipping Nf within a desired range may be inserted.
The first embodiment has an effect of preventing moiré from being emphasized, since edge emphasis can be suppressed for a halftone area with a relatively large variation time count. However, when moiré has already occurred before the image adjusting process, moiré can be prevented from being worsened any further upon execution of the first embodiment, but it is difficult to eliminate moiré. Since the second embodiment can strongly apply smoothing to the halftone area with a relatively large variation time count, moiré can be effectively eliminated compared to the first embodiment. Since this embodiment can weaken smoothing to be applied to the text area with a small variation time count, the sharpness of characters is never impaired. Like in the conventional process, when an image is separated into halftone and text areas, and when smoothing is applied to the halftone area and is not applied to the text area, if a part of the halftone area is determined as a halftone area, and its remaining part is determined as a text part, switching of the smoothing processes becomes conspicuous on an image. Since the second embodiment can adaptively define the smoothing level which progressively changes according to the variation time count, switching of the smoothing processes as an issue of the conventional process can be obscured.
The first embodiment has exemplified the case in which the edge emphasis process by means of the filter process is executed at the adaptive level.
<Adjusting Level Defining STEP 2510: Determine Maximum and Minimum Luminance Positions>
The CPU 11 determines pixel positions with maximum L and minimum L from seven pixels of L in the edge direction determined in STEP 704 of the four directions extracted in STEP 702.
<Adjusting Level Defining STEP 2511: Calculate Variation Acceleration>
The CPU 11 calculates a variation acceleration Lap of three pixels from Grd of the edge direction calculated in STEP 703 in the edge direction determined in STEP 704. The CPU 11 calculates the variation acceleration by:
Lap(i)=Grd(i+1)−Grd(i−1) (15)
where Grd(i−1) is a pixel before pixel Grd(i), and Grd(i+1) is a pixel after that pixel.
Note that the calculation method of the variation acceleration is not limited to this. For example, a difference between neighboring Grd data may be calculated.
<Adjusting Level Defining STEP 2512: Determine Substitute Pixel Position>
The CPU 11 determines a substitute pixel position based on the pixel positions with maximum L and minimum L determined in STEP 2510 and the variation accelerations Lap calculated in STEP 2511. As shown in
<Adjusting Level Defining STEP 2513: Define Substitute Level Based on Absolute Value of Variation Acceleration>
The CPU 11 adaptively defines a substitute level C1 in accordance with the absolute value of the variable acceleration calculated in STEP 2511.
C1=(absolute value of variation acceleration−ninth threshold)/(10th threshold−ninth threshold) (16)
<Adjusting Level Defining STEP 2514: Define Substitute Level Based on Variation Time Count>
The CPU 11 adaptively defines a substitute level Cz in accordance with the variation time count calculated in STEP 706. The CPU 11 adaptively defines Cz based on characteristics shown in
Cz=(12th threshold−variation time count)/(12th threshold−11th threshold) (17)
<Adjusting Level Defining STEP 2515: Define Substitute Level Based on Variation>
The CPU 11 adaptively defines a substitute level Ce in accordance with the variation calculated in STEP 705. The CPU 11 adaptively defines Ce based on characteristics shown in
Ce=(variation−13th threshold)/(14th threshold−13th threshold) (18)
<Adjusting Process STEP 2706: Calculate Substitute Amount>
The CPU 11 calculates a substitute amount using the pixel value at the substitute pixel position determined in STEP 2512. The CPU 11 extracts RGB values at the substitute pixel position determined in STEP 2512 from the 7×7 RGB areas defined in STEP 701. Let N0 be the pixel value of interest, C0 be the pixel value at the substitute pixel position, and ΔC be the substitute amount. Then, ΔC can calculated using:
ΔC=C0−N0 (19)
<Adjusting Process STEP 2707: Adjust Substitute Amount by C1>
The CPU 11 adjusts the substitute amount ΔC calculated in STEP 2706 by the substitute level C1 defined in STEP 2513. The CPU 11 calculates the adjusted substitute amount ΔC1 using:
ΔC1=C1×ΔC (20)
By the process in STEP 2707, the substitution that suppresses generation of jaggy can be applied.
<Adjusting Process STEP 2708: Adjust Substitute Amount by Cz>
The CPU 11 adjusts the substitute amount ΔC1 calculated in STEP 2707 by the substitute level Cz defined in STEP 2514. The CPU 11 calculates the adjusted substitute amount ΔCz using:
ΔCz=Cz×ΔC1 (21)
By the process in STEP 2708, the substitution that can suppress generation of jaggy can be applied by strengthening the substitute level for the bold line area, and by weakening that for the thin line area.
<Adjusting Process STEP 2709: Adjust Substitute Amount by Ce>
The CPU 11 adjusts the substitute amount ΔCz calculated in STEP 2708 by the substitute level Ce defined in STEP 2515. The CPU 11 calculates the adjusted substitute amount ΔCe using:
ΔCe=Ce×ΔCz (22)
By the process in STEP 2709, an edge area of a character or the like is relatively strongly substituted to enhance sharpness, and a flat area is relatively weakly substituted to prevent roughening.
<Adjusting Process STEP 2710: Completion of Substitution Process>
The CPU 11 calculates a pixel value Nc of interest edge-emphasized by means of filtering and substitution by adding the substitute amount ΔCe calculated in STEP 2709 to the edge emphasis filter-processed value Ne of the pixel of interest, as given by:
Nc=Ne+ΔCe (23)
Note that a process for clipping Nc within a desired range may be inserted.
The effects of the aforementioned third embodiment will be described below. Since the edge emphasis process using the substitution process of the third embodiment is executed in addition to that by means of the edge emphasis filter of the first embodiment, an effect of enhancing the sharpness more than the first embodiment can be obtained. When the bold line area is not to be bordered in the first embodiment, it is weakly edge-emphasized. However, the third embodiment can provide an effect of emphasizing an edge more than the first embodiment while preventing bordering. Since a halftone area with a relatively large variation time count can be weakly substituted, moiré is never emphasized. Since a photo area with a relatively small variation is weakly substituted, it can be prevented from being roughened. Since the third embodiment can adaptively define the substitute level according to the variation acceleration, variation time count, and variation, switching of the substitute processes can be obscured on an image compared to application of substitution to a text area extracted by image area separation of text and halftone areas by the prior art.
The first to third embodiments have explained the edge emphasis process that enhances sharpness and the smoothing process that eliminates moiré so as to eliminate blurring and moiré generated upon scanning a document. However, there is another issue upon scanning a document. Upon scanning a black character by the scanning unit, R, G, and B do not always assume the same values. As a result, a black density drop and an increase in saturation occur. Such drawbacks deteriorate the quality of the black character. This embodiment will explain a process that defines R, G, and B values to be closer to each other obtained by scanning black. The process that defines R, G, and B values to be closer to each other will be referred to as an achromatization process, and the level of the achromatization process will be referred to as an achromatization level hereinafter. In the following description, the achromatization level is adaptively defined as in the first to third embodiments.
<Adjusting Level Defining STEP 2816: Calculate Saturation>
The CPU 11 calculates a saturation for the pixel of interest of the 7×7 RGB areas defined in STEP 701. The CPU 11 calculates color average values of 3×3 areas having the pixel of interest as the center. Let AR, AG, and AB be the average values of R, G, and B. Then, the CPU 11 calculates, as a saturation, a maximum value of |AR−AG|, |AG−AB|, and |AB−AR|. Note that the present invention is not limited to such specific saturation calculation method. In this case, the saturation is calculated from the averages of the 3×3 areas, but it may be calculated from areas within the process area size defined in STEP 701. This embodiment calculates the color space based on RGB. Alternatively, a block may be converted into a luminance color difference space, and the saturation may be calculated as a distance from the luminance axis using color difference components. Furthermore, the saturation may be calculated based on a value obtained after the edge emphasis and smoothing processes using Nc calculated in the third embodiment.
<Adjusting Level Defining STEP 2817: Define Achromatization Level Based on Saturation>
The CPU 11 adaptively defines an achromatization level Ks in accordance with the saturation calculated in STEP 2816.
Ks=(16th threshold−saturation)/(16th threshold−15th threshold) (24)
<Adjusting Level Defining STEP 2818: Define Achromatization Level Based on Variation Time Count>
The CPU 11 adaptively defines an achromatization level Kz in accordance with the variation time count calculated in STEP 706. The CPU 11 adaptively defines Kz based on characteristics shown in
Kz=(18th threshold−variation time count)/(18th threshold−17th threshold) (25)
<Adjusting Level Defining STEP 2819: Define Achromatization Level Based on Variation>
The CPU 11 adaptively defines an achromatization level Ke in accordance with the variation calculated in STEP 705. The CPU 11 adaptively defines Ke based on characteristics shown in
Ke=(variation−19th threshold)/(20th threshold−19th threshold) (26)
<Adjusting Process STEP 3011: Calculate Achromatization Amount>
The CPU 11 calculates an achromatization amount ΔK using Nc calculated in STEP 2710 by:
ΔK=NcG−NcP (27)
where NcG is Nc of a G component, and NcP is Nc of an R or B component.
<Adjusting Process STEP 3012: Adjust Achromatization Amount by Ks>
The CPU 11 adjusts the achromatization amount ΔK calculated in STEP 3011 by the achromatization level Ks defined in STEP 2817. The CPU 11 calculates the adjusted achromatization amount ΔKs using:
ΔKs=Ks×ΔK (28)
By the process in STEP 3012, an image signal near the luminance axis can be closer to the luminance axis.
<Adjusting Process STEP 3013: Adjust Achromatization Amount by Kz>
The CPU 11 adjusts the achromatization amount ΔKs calculated in STEP 3012 by the achromatization level Kz defined in STEP 2818. The CPU 11 calculates the adjusted achromatization amount ΔKz using:
ΔKz=Kz×ΔKs (29)
By the process in STEP 3013, relatively strong achromatization is applied to a text area with a smaller variation time count to blacken a character, and relatively weak achromatization is applied to halftone and photo areas with a larger variation time count to suppress a change in tint.
<Adjusting Process STEP 3014: Adjust Achromatization Amount by Ke>
The CPU 11 adjusts the achromatization amount ΔKz calculated in STEP 3013 by the achromatization level Ke defined in STEP 2819. The CPU 11 calculates the adjusted achromatization level ΔKe using:
ΔKe=Ke×ΔKz (30)
By the process in STEP 3014, strong achromatization is applied to an edge area of a character to blacken the character, and weak achromatization is applied to an image with a relatively weak edge like a photo to suppress a change in tint.
<Adjusting Process STEP 3015: Completion of Achromatization Process>
The CPU 11 calculates a pixel value Nk of interest that has undergone the filter process, substitution process, and achromatization process by adding the achromatization amount ΔKe calculated in STEP 3014 to the pixel value Nc of interest edge-emphasized by means of filtering and substation, as given by:
Nk=Nc+ΔKe (31)
Note that a process for clipping Nk within a desired range may be inserted.
The first to third embodiments can provide an effect of enhancing the sharpness. However, these embodiments cannot make the values of image signals of respective colors be close to the same value, and a black character cannot appear to gleam black. Since the fourth embodiment can adaptively achromatize the pixel of interest according to the saturation, it can provide an effect with which a black character with a pixel value near the luminance axis has quality to gleam black. Since the achromatization level can be changed according to the variation time count and variation, only a text area may be achromatized, so that tints of halftone and photo areas are left unchanged. Since the fourth embodiment can set an achromatization level according to the saturation, variation time count, and variation, switching of the achromatization processes can be obscured on an image compared to application of achromatization after image area separation of text and achromatic areas in the conventional process.
In the description of the first to fourth embodiments, the edge emphasis, smoothing, and achromatization amounts are adaptively adjusted. A case will be exemplified below wherein
On the other hand, the first to fourth embodiments adaptively determine the adjusting level using the variation time count and variation, and also the variation acceleration and saturation. When the adjusting level is replaced by an image area, this embodiment can adaptively separate image areas in accordance with the variation time count and variation. For example, as the product of Fz and Fe is larger, a pixel which is more likely to belong to a text area can be determined. As the product is smaller, a pixel which is more likely to belong to a halftone or photo area can be determined. In addition, using the variation acceleration and saturation as well, the proximity to the edge center and that to the luminance axis can also be determined, and image areas can be separated more finely.
The embodiments of the present invention have been explained in detail. The present invention may be applied to either a system constituted by a plurality of devices, or an apparatus consisting of a single device.
Note that the present invention includes a case wherein the invention is achieved by directly or remotely supplying a program that implements the functions of the aforementioned embodiments to a system or apparatus, and reading out and executing the supplied program code by a computer of that system or apparatus. Therefore, the technical scope of the present invention includes the program code itself installed in a computer to implement the functional processes of the present invention using the computer.
In this case, the form of program is not particularly limited, and an object code, a program to be executed by an interpreter, script data to be supplied to an OS, and the like may be used as long as they have the program function.
As a recording medium for supplying the program, for example, a floppy® disk, hard disk, optical disk, and magneto-optical disk may be used. Also, MO, CD-ROM, CD-R, CD-RW, magnetic tape, nonvolatile memory card, ROM, DVD (DVD-ROM, DVD-R), and the like may be used.
As another use method, a connection is established to the Internet site using a browser of a client PC, and the program itself according to the present invention or a file that further includes an automatic installation function may be downloaded to a recording medium such as a hard disk or the like. Also, the program code that forms the program of the present invention may be segmented into a plurality of files, which may be downloaded from different homepages. That is, the present invention includes a WWW server which makes a plurality of users download a program required to implement the functional processes of the present invention by the computer. Furthermore, a storage medium such as a CD-ROM or the like, which stores the encrypted program of the present invention, may be delivered to the users. The user who has cleared a predetermined condition may be allowed to download key information that decrypts the program from a homepage via the Internet, and the encrypted program may be executed using that key information to be installed on a computer, thus implementing the present invention.
Moreover, the functions of the aforementioned embodiments can be implemented by some or all of actual processes executed by an OS or the like which runs on a computer based on instructions of the program.
In addition, the scope of the present invention includes a case in which the program according to the present invention is written in a memory of a function expansion unit of a PC, and a CPU equipped on that function expansion unit executes some or all of actual processes.
According to the present invention, since the adjusting level can be changed based on the variation and variation time count, bad effects that may be caused by the image adjusting processes can be eliminated, and the image adjusting processes can be executed more accurately to improve the image quality.
While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. The scope of the following claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions.
This application claims the benefit of Japanese Patent Application No. 2006-180378, filed on Jun. 29, 2006, which is hereby incorporated by reference herein in its entirety.
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