JP3573085B2 - Image processing apparatus and image processing method - Google Patents

Description

[0001]
TECHNICAL FIELD OF THE INVENTION
The present invention relates to an image processing apparatus and an image processing method capable of interpolating a low-quality image into a high-quality image, for example.
[0002]
[Prior art]
For example, when converting a low-quality digital image to a high-quality image, converting a low-resolution image to a high-resolution image, or enlarging an image, a new pixel is placed between the original pixels. Image interpolation such as inserting a pixel is performed. As a method for interpolating a digital image, for example, a nearest neighbor interpolation method (also referred to as a nearest neighbor interpolation method, a zero-order hold method, a Nearest-Neighbor method), a linear interpolation method (linear interpolation method, bilinear interpolation method, Bi-Linear method) A third-order convolution interpolation method (also referred to as a three-dimensional convolution method, a Bi-Cubic method, or a Cubic Convolution method) is known.
[0003]
[Problems to be solved by the invention]
Each of the above-described interpolation methods has a basic concept of interpolation using a sinc function based on the sampling theorem, and is theoretically correct only when the original image is composed of frequency components equal to or less than half the Nyquist frequency. However, since the frequency components contained in the actual original image are infinitely large, the above-described interpolation methods cannot restore the high-frequency components contained in the original image.
[0004]
Therefore, a frequency conversion method has been proposed as a method for interpolating the high-frequency components lost in the sampling process. As the frequency conversion method, the frequency components band-limited in the frequency domain are projected to the real space, only the restricted range of all the real space components is projected to the frequency space, and the band-limited portion of the total frequency components is Is replaced with a known original frequency component, and the operation of projecting again to the real space is infinitely repeated, and the Gerchberg-Papolis iterative method (GP method) is well known. In general, the computational burden is reduced by using DCT computation for frequency conversion (IM-GPDCT method).
[0005]
However, it is necessary to repeat the DCT operation and the inverse DCT operation until an appropriate high-frequency component is obtained, so that the processing time becomes long. In addition, noise may be emphasized, ringing may occur, and image quality may be degraded.
[0006]
SUMMARY An advantage of some aspects of the invention is to provide an image processing apparatus and an image processing method that can obtain a high-quality image at higher speed.
[0007]
[Means for Solving the Problems]
In order to achieve the above object, in the present invention, each image block is acquired such that adjacent blocks overlap each other, and only image blocks having a degree of fractal similarity exceeding a predetermined reference value among each image block are included in the image blocks. The image is replaced with a similar image.
[0008]
That is, in the present invention, an original image holding unit for holding an original image and an image block of a predetermined size are acquired from the held original image such that adjacent image blocks overlap by a predetermined amount. An image block acquisition unit, a similarity determination unit that determines whether or not the degree of similarity exceeds a predetermined reference value for each of the acquired image blocks; and a degree of the similarity determined by the similarity determination unit. For each of the image blocks determined to exceed the predetermined reference value, a similar image detecting unit that detects a similar image block from the original image, and the detected similar image blocks correspond to each other. Image replacement means for replacing each of the image blocks, and the degree of similarity determined by the replaced similar image block and the similarity determination means. Image adding means for superimposing an image block determined to be equal to or less than the predetermined reference value, and image adjusting means for adjusting an image of an overlapping portion of the block added by the image adding means. Features.
[0009]
For example, digital data of an original image is held by an original image holding unit such as a memory. The image block acquisition unit acquires an image block of a predetermined size (n × m pixels, n = m may be used) from the held original image. Here, each image block is obtained such that adjacent image blocks overlap by a predetermined amount. For example, by acquiring, for each pixel of the original image, an image block of a predetermined size substantially at the center of the pixel, adjacent image blocks overlap by a predetermined amount.
[0010]
The similarity determining means determines whether the degree of similarity of each image block exceeds a predetermined reference value. When a fractal similar image block is used as the similar image block, it can be expressed as “a degree of fractal property”. As the degree of similarity, the strength of the edge of the image block can be used. This is because an image having higher fractal self-similarity has a property of increasing edge strength.
[0011]
The similar image detecting means detects a similar image block similar to each image block from the original image for each image block for which the degree of similarity is determined to exceed a predetermined reference value. It is possible to detect a similar image block similar to an image block from an original image by using a property that a part of the image is a reduced form of a larger image around the image, that is, fractal similarity. it can. Here, it should be noted that similarity image blocks are not detected for each image block whose degree of similarity is equal to or less than a predetermined reference value.
[0012]
Each of the detected similar image blocks is replaced by a corresponding image block by the image replacement means. Each image block having a low degree of similarity is used as it is. The image adding means adds the similar image block and the image block whose degree of similarity is determined to be equal to or less than a predetermined reference value. As a result, roughly classified, there are three types of overlaps: a place where similar image blocks overlap, a place where similar image blocks and image blocks overlap, and a place where image blocks overlap.
[0013]
Since the adjacent image blocks are acquired so as to overlap each other by a predetermined amount, the blocks to be added also overlap the adjacent blocks by a predetermined amount. Therefore, the image adjusting means adjusts the value of the overlapping portion of the block. For example, the image can be adjusted by adding and averaging the values of the overlapping pixels. However, the present invention is not limited to this. For example, an average may be obtained by weighting.
[0014]
The similar image detecting means may detect the similar image block with a size larger than the size of the image block, and reduce the size of the similar image block to be equal to the size of the image block.
[0015]
For example, assuming that the size of an image block is n × m and the size of a similar image block is (kn) × (km), the similar image block is reduced by a factor of k to obtain a similar image block. The size of the image blocks can be matched.
[0016]
Alternatively, the similar image detecting means detects a similar image block having the same size as the image block by thinning out pixels from a predetermined area set in the original image with a size larger than the size of the image block. It may be something.
[0017]
In the above example, from a similar image block having (kn) × (km) pixels, 1 / k ² Is extracted, and the remaining pixels are discarded, whereby a similar image block having the same size as an image block having n × m pixels can be obtained.
[0018]
The similar image detecting means can detect similar image blocks similar to each image block from the original image under predetermined conditions set in advance.
[0019]
Examples of the predetermined condition include a setting condition of a search area for detecting a similar image block. Then, the similar image detecting means may be configured to detect a similar image block similar to the image block in a search area set to be smaller than the original image.
[0020]
Although a similar image block can be searched for in the entire original image, the wider the search area, the longer it takes to detect a similar image block. By setting the search area smaller than the original image, the processing time can be reduced. Although it depends on the characteristics of the original image (natural image or text image, etc.) and the size of the image block, if there is a high possibility that a similar image block exists around the image block, the similar image block is detected. Can reduce the time it takes to do so. Therefore, the search area may be set around the image block.
[0021]
Examples of the predetermined condition include a setting condition of a search area for detecting a similar image block and a parameter condition of an image operation for detecting the similar image block. Then, the similar image detecting means acquires a plurality of candidate image blocks while changing image operation parameters within a predetermined search area, and selects a candidate image block most similar to the image block from among the candidate image blocks. You may comprise so that it may detect as a similar image block similar to an image block.
[0022]
The “image operation” includes, for example, affine transformation for performing parallel movement, enlargement, reduction, and rotation of an image. The “parameters for image operation” include, for example, the amount of parallel movement, enlargement ratio, reduction ratio, and rotation angle. The similar image detecting means acquires a plurality of candidate image blocks while changing image operation parameters within a predetermined search area. Then, of these plurality of candidate image blocks, the one that is most similar to the image block is detected as a similar image block.
[0023]
The similar image detecting means determines the similarity of each candidate image block while changing the parameters of the image operation, and holds only the parameters used for obtaining the candidate image block most similar to the image block, and stores the stored parameters. May be used to detect similar image blocks.
[0024]
When each of the candidate image blocks is stored, the memory area for storing each of the candidate image blocks becomes large, but by storing only the parameters necessary for obtaining the candidate image block of the minimum distance, the number of memory regions is reduced. Similar image blocks can be detected in the memory area.
[0025]
When the original image is a color image, the predetermined condition may be settable for each component of the color system.
[0026]
For example, in the case of the RGB color system, it is possible to set the location and size of the search area, the type of image operation, and parameters for each of the R (red), G (green), and B (blue) color components. In order to detect similar image blocks for each color component and luminance component in various color systems such as the YUV color system, the YIQ color system, the YCbCr color system, and the Lab color system as well as the RGB color system. May be changed. Thus, for example, the predetermined color component can be determined for each color component according to the type of product (digital camera, printer, scanner, etc.) to which the image processing apparatus is applied, the characteristics of the original image (whether or not the image is a natural image), and user preferences. Conditions can be changed.
[0027]
The present invention can be understood as a recording medium on which a computer program is recorded. The program can be fixed to various tangible recording media such as a hard disk, a floppy disk, and a memory. The present invention is not limited to this, and a communication medium may be used, such as downloading a predetermined program from a server on a network.
[0028]
BEST MODE FOR CARRYING OUT THE INVENTION
Hereinafter, an embodiment of the present invention will be described with reference to FIGS.
[0029]
1. First embodiment
FIGS. 1 to 6 are block diagrams showing the configuration of the image processing apparatus according to the first embodiment of the present invention.
[0030]
The image processing apparatus 1 performs predetermined image processing on an original image input from the original image holding unit 2, and outputs a processed image to the output image holding unit 13. The original image holding unit 2 and the output image holding unit 13 are configured by a memory or the like. The original image holding unit 2 and the output image holding unit 13 may be incorporated as a configuration of the image processing device 1 or may be configured separately from the image processing device 1. For example, when the original image data is read from the PC card on which the original image is recorded, and the processed image is written back to the empty area of the PC card, the PC card becomes the original image holding unit 2 and the output image holding unit 13. .
[0031]
The local area image obtaining section 3 as “image block obtaining means” obtains a local area image of a predetermined size for each pixel of the original image. The local area image corresponds to the “image block”. As will be described later with reference to FIG. 2, since a local region image is obtained for each pixel, adjacent local region images overlap by a predetermined amount. The local area image obtaining section 3 obtains a local area image based on the local area image obtaining parameters set by the parameter setting section 4. As this parameter, for example, the size of the local area image can be cited. The target pixel pointer 5 detects the position of the pixel currently being processed (target pixel).
[0032]
The affine transformed image acquisition unit 6 is similar to each of the local region images determined to have an edge intensity exceeding a predetermined reference value among the acquired local region images based on the instruction from the edge intensity determination unit 14. The image is detected and acquired from the original image. The affine transformation image acquisition unit 6 operates an image existing around the local region image based on the affine transformation parameters set by the affine transformation parameter setting unit 7 to detect an affine transformation image similar to the local region image. It is supposed to. An affine transformed image similar to the local region image corresponds to a “similar image block”.
[0033]
The affine-transformed image acquisition unit 6 acquires an affine-transformed image having a size larger than the local region image, for example, a size that is twice as long and wide. The obtained affine transformed image is reduced by the reducing unit 8 so as to have the same size as the local region image. The similarity calculating unit 9 calculates the distance between the local region image acquired by the local region image acquiring unit 3 and the affine transformed image acquired by the affine transformed image acquiring unit 6 and reduced by the reducing unit 8, and the similarity between the two is calculated. , To calculate the similarity. In the distance calculation, an eigenvector distance may be calculated, or a square mean may be simply taken. Here, the affine transformed image acquisition unit 6, the reduction unit 8, and the similarity calculation unit 9 correspond to “similar image detection means”.
[0034]
Then, the affine transformed image determined to be most similar by the similarity calculating unit 9 is replaced with a corresponding local area image by the replacing unit 10 as “image replacing means”.
[0035]
The adding unit 11 as an “image adding unit” adds the replaced affine transformed image and the non-replaced local region image. That is, the entire local region image acquired from the original image is replaced with an affine transformed image similar to the self image and added, or the original local region image is added as it is. Since the local region images are acquired such that the adjacent local region images overlap each other, the replaced affine transformed image and the original local region image are added such that the adjacent images overlap by a predetermined amount, and the adding unit 11 Is added by The averaging processing unit 12 as “image adjusting means” adjusts by averaging the values of the portion where each image overlaps. As an image adjustment method of the overlapping portion, a simple average may be taken, or a weighted average may be taken. The image adjusted in this way is held in the output image holding unit 13. After image adjustment, enlargement processing, reduction processing, rotation processing, color conversion processing, and the like may be performed.
[0036]
The edge strength determination unit 14 as “similarity determination means” determines the degree of fractalness of each of the acquired local region images. The affine-transformed image is detected by the affine-transformed image acquiring unit 6 for the local region image determined to have a high degree of fractal property. The local region image having a low degree of fractal property is added as it is to the affine transformed image. That is, instead of acquiring affine transformed images for all local region images acquired from the original image, affine transformed images are acquired only for local region images having a high degree of fractal property. As the “degree of fractal property”, edge strength can be used. This will be described later with reference to FIG.
[0037]
FIG. 2 is an explanatory diagram illustrating an outline of an image processing method by the image processing apparatus. As shown in FIG. 2A, a local region image is set for each pixel of the original image, and an affine transformed image is obtained for each local region image having a high edge strength among the local region images.
[0038]
For example, an n × n size local region image is acquired with the image of interest related to the current process substantially at the center. The figure illustrates a case where n = 2. The affine transformed image is acquired so as to be larger than the local area image size, specifically, for example, twice as large in the vertical and horizontal directions (2n × 2n). In the embodiments, the local region image and the affine transformed image are described as squares unless otherwise specified, but the present invention is not limited to this. Each image can be set to another polygon such as a rectangle or a parallelogram.
[0039]
Here, an affine transformed image similar to the local region image can be searched for from the entire original image. However, the processing time becomes longer as the search area is expanded. Further, depending on the size of the image block, the characteristics of the original image, and the like, a similar image block may be more likely to be found around the image block. In particular, the present invention does not divide the entire original image into a plurality of image blocks that do not overlap each other, and obtain image blocks similar to each image block, but set so that a part of each adjacent image block overlaps. Therefore, the number of image blocks increases, and the number of times of detecting similar image blocks increases. Therefore, if the search area (search range) of the similar image block is extended to the entire original image, the processing time may be longer.
[0040]
Therefore, in the present embodiment, a similar image block is searched for only a part of the original image and further around the image block, instead of searching the entire original image. The size of the search area can be dynamically changed according to the properties of the original image and the like. When the size of the image block is n × m, the size of the original image is Xmax and Ymax, and the size of the search area is αn × βm, the following relationship is established.
[0041]
1 <α <Xmax / n (Equation 1)
1 <β <Ymax / m (Equation 2)
The values of the coefficients α and β that determine the size of the search area can be set arbitrarily within a range that satisfies Equations 1 and 2. However, the present invention is not limited to this, and the entire original image may be searched.
[0042]
As shown in FIG. 2B, an affine transformed image (“candidate image block”) acquired at a size twice as large as the local region image is reduced to have the same size as the local region image. Then, the distance between the reduced affine transformed image and the local region image is calculated, and the similarity and the similarity are determined. An affine transformed image having the highest similarity is replaced with the local region image from among the affine transformed images obtained for a certain local region image.
[0043]
As shown in FIG. 2C, since the local region images are acquired such that the adjacent images overlap each other, the affine transformed images replaced with the respective local region images also have adjacent images. Duplicate. As shown in the figure, when a local area image of 2 pixels × 2 pixels is acquired by shifting one pixel at a time, focusing only on the x direction, a maximum of four images overlap. Similarly, a maximum of four local region images overlap in the y direction. Therefore, the overlapping part is adjusted by taking a simple average of the values of each pixel.
[0044]
In the present embodiment, an affine transformed image is acquired only for a local region image having a high edge strength (fractal property), instead of acquiring an affine transformed image for the entire local region image. Therefore, all four images shown in FIG. 2C are not necessarily affine transformed images. The affine transformed images, the original local region images, and the affine transformed image and the local region image may overlap each other.
[0045]
FIG. 3 is an explanatory diagram showing an image operation method and parameters when acquiring an affine transformed image. As shown in FIG. 3A, the image can be translated by Sx and Sy. Further, as shown in FIG. 3B, the image can be rotated by the angle θ. Further, as shown in FIGS. 3C and 3D, the image can be enlarged or reduced Ex times in the x direction, or enlarged or reduced Ey times in the y direction.
[0046]
Next, the operation of the present embodiment will be described with reference to FIGS. Hereinafter, steps are abbreviated as “S”.
[0047]
First, FIG. 4 is a flowchart showing the overall flow of the image processing. In S1, (0, 0) is set to the coordinates (x, y) of the target pixel, and conversion is started from the first pixel of the original image. Next, the edge strength of the local area image centering on the target pixel is calculated (S2). As shown in FIG. 5, taking a region of 3 × 3 pixels centered on the target pixel Px5 as an example, the level difference (| Px1 + Px2 + Px3 | − | Px7 + Px8 + Px9 |) of the columns located above and below the target pixel The level difference (| Px1 + Px4 + Px7 |-| Px3 + Px6 + Px9 |) of the columns located on the left and right of the pixel is detected (S2), and it is determined whether the level difference between the upper, lower, left, and right directions is greater than a predetermined reference value th (S3). . That is, it is determined whether or not the degree of fractal property is strong based on the edge strength around the target pixel.
[0048]
Here, it is not always necessary to match the size of the region for edge strength determination with the size of the local region image. For example, when acquiring a local area image of 4 × 4 pixels around the pixel of interest, the edge strength can be determined in an area of 3 × 3 pixels centering on the pixel of interest, or 5 × 5 pixels It is also possible to determine the edge strength in the above area. In the present embodiment, a description will be given assuming that the size of the edge strength determination area matches the size of the local area image.
[0049]
When the calculated edge strength exceeds the predetermined reference value th (S3: YES), fractal interpolation described later with reference to FIG. 6 is performed (S4). On the other hand, if the edge strength is equal to or smaller than the predetermined reference value (S3: NO), a local region image is obtained (S5), and the obtained local region image is added as it is (S6).
[0050]
Then, the pixel of interest is moved to the next pixel (S7), and it is determined whether any of the affine transformed image or the local region image has been acquired for all the pixels of the original image (S8). If the processing of the entire original image has not been completed, the process returns to S2 and the above-described processing is repeated.
[0051]
When a local region image or an affine transformed image similar to the local region image corresponding to each pixel of the original image is obtained and added (S8: YES), the overlapping portion of each image is averaged, and the output image is averaged. (S9). When the image converted in this way is enlarged or the like, conventional interpolation processing such as linear interpolation can be further performed.
[0052]
Note that, in the present embodiment, a case where the processing is performed on the entire original image is exemplified, but the present invention is not limited to this. The image processing according to the present invention can be partially applied only to a predetermined area specified by a user or the like.
[0053]
Next, FIG. 6 is a flowchart showing the flow of the fractal interpolation process indicated as S4 in FIG.
[0054]
First, the maximum value is set to the “minimum distance” for determining the similarity between the local area image and the affine transformed image (S11). Then, a local region image having the pixel of interest substantially at the center is acquired (S12), and initial values are set to the respective parameters (Sx, Sy, Ex, Ey, θ) for affine transformation (S13). The same applies to other steps described later, but the processing order of the steps can be changed as long as the processing is not affected. That is, the processing order of S11 to S13 does not matter.
[0055]
When an affine transformed image is acquired based on the set parameters (S14), the acquired affine transformed image is reduced so as to have the same size as the local region image (S15). Then, the distance between the local area image and the affine transformed image of the same size corresponding to the local area image is calculated (S16), and the calculated distance is calculated based on the value set in the similarity determination parameter “minimum distance”. Is smaller or not, that is, whether or not they are more similar (S17). If the latest distance calculation result is smaller than the "minimum distance", the latest affine transformation parameters and the distance values are held (S18). If the latest distance is not smaller than the “minimum distance”, the acquired affine transformed image does not resemble the local region image, and thus does not hold the value of each parameter and distance.
[0056]
Then, each parameter is changed by a predetermined amount (S19), and it is determined whether or not each parameter exceeds the variable range (S20). That is, an affine transformation image is acquired while changing each parameter from the initial value to the upper limit value, and the distance to the local region image is calculated (S14 to S20). Therefore, in S18, among the affine transformed images obtained within the variable range of each parameter, the affine transformed parameters of the affine transformed image most similar to the local region image related to the current target pixel and the distance thereof are held. .
[0057]
When each parameter is changed to the upper limit, an affine transformed image that is most similar to the local region image is acquired based on each parameter held in S18 (S21). Then, the acquired affine transformed image is reduced to the same size as the local area image (S22), and is added to the previously acquired affine transformed image (S23). The addition means storing the digital data of the affine transformed image at a predetermined position in the memory area.
[0058]
Thereby, of all the local region images obtained from the original image, for a local region image having a high degree of fractal similarity, an affine transformation image similar to the local region image is detected and replaced in the original image, Is added. On the other hand, for a local region image with a low degree of fractal similarity, an affine transformed image is not obtained, and the original local region image is added as it is.
[0059]
The present embodiment configured as above has the following effects.
[0060]
First, a local region image is acquired such that adjacent images overlap by a predetermined amount, and for each local region image having high fractal similarity, an affine transformed image similar to each local region image is included in the original image. , The low-quality image can be converted to a high-quality image.
[0061]
Second, since the affine transformation image is acquired only when the degree of fractalness is high, the processing time can be reduced as compared with the case where the affine transformation images are acquired for all the local region images.
[0062]
Third, since each image is overlapped by a predetermined amount and the overlapped portion is processed by averaging or the like, it is possible to prevent a sense of incongruity at a joint between images as compared with a case where the images are not overlapped. Therefore, for example, when the original image is a natural image, the quality can be improved while maintaining a natural gradation change.
[0063]
Fourth, an affine image similar to the local region image is obtained in a relatively short time because the affine image similar to the local region image is searched not from the entire original image but from the periphery of the local region image. be able to. Therefore, even when there are many affine-transformed images to be detected, it is possible to prevent the overall processing time from significantly increasing.
[0064]
Fifth, while judging the similarity between the affine transformation image and the local region image, the affine transformation parameters most similar to the local region image are held, and after the search of the search region is completed, the stored affine transformation parameters are used. Since an affine transformed image is obtained, an affine transformed image similar to a local region image can be obtained with a small amount of memory resources.
[0065]
2. Second embodiment
Next, a second embodiment of the present invention will be described with reference to FIG. In the following embodiments, the same components as those described above are denoted by the same reference numerals, and description thereof will be omitted. The feature of the present embodiment is that an affine transformed image having the same size as the local region image is obtained by thinning out the pixels and acquiring an affine transformed image.
[0066]
As shown in FIG. 7A, an affine transformed image having the same size as the local region image can be obtained by acquiring an affine transformed image with a size of 2n × 2n and reducing the acquired affine transformed image. .
[0067]
On the other hand, as shown in FIG. 7B, an affine transformed image is acquired in a size of 2n × 2n, but not all pixels in the size are acquired, but pixels are acquired every other pixel. By doing so, an affine transformed image having the same size as the local area image can be obtained without performing the reduction processing.
[0068]
3. Third embodiment
Next, a third embodiment of the present invention will be described with reference to FIG. The feature of this embodiment is that the value of the affine transformation parameter can be set for each color component based on the characteristics of the original image and the like.
[0069]
First, the user can exclusively select the color blur prevention mode or the sharp mode when performing the image modification by the image processing apparatus (S31). In the color fringing prevention mode, the fractal interpolation according to the present invention is applied to the R component and the B component of the original image expressed in the RGB color system in order to suppress color fringing. On the other hand, in the sharp mode, fractal interpolation is applied only to the G component in order to sharpen the outline and the like of the image. For example, in a digital camera or the like in which one R component pixel and one B component pixel and two G component pixels each form one unit, the color fringing prevention mode is applied to a natural image. The sharp mode is suitably used for images such as characters and diagrams.
[0070]
If the color fringing prevention mode is selected, the R plane and the B plane are set as the color planes to be subjected to fractal interpolation (S32), and the affine transformation parameters for the respective R and B planes are set (S33). On the other hand, when the sharp mode is selected, the G plane is set as the fractal interpolation target plane (S34), and the affine transformation parameters for the G plane are set (S35). Note that the initial values and the variable ranges of the parameters set in S33 and S35 may be the same.
[0071]
Then, similarly to the above, after setting the initial value of the target pixel (S36), the edge strength of the local region image centering on the target pixel is calculated (S36, S37). If the calculated edge strength exceeds the predetermined reference value th (S38: YES), fractal interpolation is performed (S39). If the edge strength is equal to or less than the reference value th (S38: NO), the acquired local region image is obtained. Is added as it is (S40, S41). The above processes are repeated until the process is completed for all pixels of the original image (S42, S43).
[0072]
When the affine transformed image or the local area image has been acquired for all the pixels of the original image (S43: YES), the respective images are added and averaged (S44). Thus, processing of one plane is completed. Therefore, it is determined whether or not there is a next plane to be processed (S45). If there is a plane to be processed, the plane is switched to the above plane and the above-described processing is performed (S46). When the processing has been completed for all the planes for which the execution of fractal interpolation has been designated, the image is combined with another plane to obtain an output image (S47).
[0073]
That is, in the color fringing prevention mode, the fractal interpolation according to the present invention is performed on the image data of the R plane and the B plane, and the image data is synthesized with the G plane to obtain an output image. In this case, the G plane may be combined after performing other interpolation processing such as linear interpolation, or may be combined without performing the interpolation processing. Similarly, in the case of the sharp mode, the fractal interpolation according to the present invention is performed only on the image data of the G plane, and is combined with the R plane and the B plane.
[0074]
Thus, image processing can be performed according to the characteristics of the original image, the user's request, and the like.
[0075]
It should be noted that those skilled in the art can make various additions, modifications, combinations, and the like within the scope of the present invention described in each of the above embodiments. For example, in each of the above embodiments, the case where a local region image is obtained for each pixel of the original image is exemplified. However, the present invention is not limited to this. For example, local local images are obtained every other pixel, such as every other pixel. Pixels may be selected at predetermined intervals within a range in which the area images overlap by a predetermined amount.
[0076]
【The invention's effect】
As described above, according to the image processing apparatus and the image processing method according to the present invention, a low-quality image can be converted into a high-quality image in a relatively short time.
[Brief description of the drawings]
FIG. 1 is a block diagram of an image processing apparatus according to a first embodiment of the present invention.
FIG. 2 is an explanatory diagram illustrating an outline of image processing.
FIG. 3 is an explanatory diagram showing an outline of an affine transformation.
FIG. 4 is a flowchart illustrating an image processing method.
FIG. 5 is an explanatory diagram showing a method of calculating edge strength.
FIG. 6 is a flowchart showing a specific process flow of fractal interpolation shown in FIG. 4;
FIG. 7 is an explanatory diagram of an image processing method according to a second embodiment of the present invention.
FIG. 8 is a flowchart of an image processing method according to a third embodiment of the present invention.
[Explanation of symbols]
1 Image processing device
2 Original image storage
3 Local area image acquisition unit
4 Parameter setting section
5 pixel pointer
6 Affine transformed image acquisition unit
7 Affine transformation parameter setting section
8 Reduction section
9 Similarity calculator
10 Replacement unit
11 Addition unit
12 Averaging unit
13 Output image holding unit
14 Edge strength judgment unit

Claims (13)

An original image holding unit for holding an original image;
An image block acquisition unit that acquires an image block of a predetermined size from the held original image so that each adjacent image block overlaps by a predetermined amount,
For each of the obtained image blocks, similarity determining means for determining whether the degree of similarity exceeds a predetermined reference value,
For each of the image blocks for which the degree of similarity is determined to be greater than the predetermined reference value by the similarity determination means, for each of the image blocks, similar image detection means for detecting similar image blocks from the original image; ,
An image replacement unit that replaces each of the detected similar image blocks with a corresponding one of the image blocks,
Image adding means for superimposing the replaced similar image block and an image block whose degree of similarity is determined to be equal to or less than the predetermined reference value by the similarity determining means,
Image adjustment means for adjusting the image of the overlapping portion of the block added by the image addition means,
An image processing apparatus comprising: The image processing apparatus according to claim 1, wherein an intensity of an edge of the image block is used as the degree of the similarity. The image processing apparatus according to claim 1, wherein the image adjustment unit averages the image of the overlapping portion. The image processing apparatus according to any one of claims 1 to 3, wherein the image block obtaining means obtains, for each pixel of the original image, an image block of a predetermined size having the pixel substantially at the center. . 2. The similar image detecting means detects the similar image block with a size larger than the size of the image block, and reduces the size of the similar image block so as to be equal to the size of the image block. The image processing device according to claim 4. The similar image detection unit detects a similar image block having the same size as the image block by thinning out pixels from a predetermined area set in the original image with a size larger than the size of the image block. The image processing apparatus according to claim 1, wherein the image processing apparatus performs processing. The said similar image detection means detects each similar image block similar to each said image block from the said original image under predetermined conditions set beforehand, respectively, The Claims 1 to 6 characterized by the above-mentioned. An image processing apparatus according to claim 1. The predetermined condition is a setting condition of a search area for detecting the similar image block,
The image processing apparatus according to claim 7, wherein the similar image detecting unit is configured to detect the similar image block similar to the image block in a search area set to be smaller than the original image. The predetermined condition is a setting condition of a search area for detecting the similar image block and a parameter condition of an image operation for detecting the similar image block,
The similar image detecting means obtains a plurality of candidate image blocks in a predetermined search area while changing image operation parameters, and selects a candidate image block most similar to the image block from among the candidate image blocks. The image processing apparatus according to claim 7, wherein the image processing apparatus detects the image block as a similar image block similar to the image block. The similar image detecting means determines the similarity of each of the candidate image blocks while changing the parameters of the image operation, and holds only the parameters used for acquiring the candidate image block most similar to the image block, The image processing device according to claim 9, wherein the similar image block is detected based on the stored parameters. The image processing apparatus according to claim 7, wherein when the original image is a color image, the predetermined condition can be set for each component of a color system. Each image block is sequentially acquired from the original image so that adjacent image blocks overlap by a predetermined amount,
For each of the sequentially acquired image blocks, the degree of similarity is determined,
For each image block whose degree of similarity exceeds a predetermined reference value, a similar image block similar to each image block is detected from the original image, respectively.
Each of the detected similar image blocks is replaced with a corresponding one of the image blocks,
Each of the replaced similar image blocks and the degree of similarity are superimposed on each image block equal to or less than the predetermined reference value,
Adjusting and outputting the overlapping portion of each block,
An image processing method comprising: In a recording medium recording an image processing program for causing a computer to execute image processing,
A function of acquiring image blocks of a predetermined size from the held original image such that adjacent image blocks overlap by a predetermined amount,
For each of the acquired image blocks, a function to determine whether the degree of similarity exceeds a predetermined reference value,
For each of the image blocks for which the degree of similarity is determined to exceed the predetermined reference value, a function of detecting a similar image block similar to the original image,
A function of replacing each of the detected similar image blocks with a corresponding one of the image blocks,
A function of superimposing the replaced similar image block and the image block whose degree of similarity is determined to be equal to or less than the predetermined reference value,
A function of adjusting an image of an overlapping portion of the added block;
A computer-readable recording medium storing a computer program for realizing the program on a computer in a form readable and understandable by the computer.

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