KR20130081080A - Apparatus and method for color image boundary clearness - Google Patents

Apparatus and method for color image boundary clearness Download PDF

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KR20130081080A
KR20130081080A KR1020120002051A KR20120002051A KR20130081080A KR 20130081080 A KR20130081080 A KR 20130081080A KR 1020120002051 A KR1020120002051 A KR 1020120002051A KR 20120002051 A KR20120002051 A KR 20120002051A KR 20130081080 A KR20130081080 A KR 20130081080A
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image
boundary
pixel
value
weight
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이교윤
호요성
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광주과학기술원
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/46—Colour picture communication systems
    • H04N1/56—Processing of colour picture signals
    • H04N1/58—Edge or detail enhancement; Noise or error suppression, e.g. colour misregistration correction
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N5/00—Details of television systems
    • H04N5/222—Studio circuitry; Studio devices; Studio equipment
    • H04N5/2224—Studio circuitry; Studio devices; Studio equipment related to virtual studio applications
    • H04N5/2226—Determination of depth image, e.g. for foreground/background separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20024—Filtering details
    • G06T2207/20028—Bilateral filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20076—Probabilistic image processing

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Abstract

PURPOSE: An image boundary clarification apparatus and a method thereof are provided to improve reliability of a depth map, by calculating a weighted value using random walk probability and applying the calculated weighted value to a bilateral filter. CONSTITUTION: A block separation part separates an image into a block unit of fixed size (S710). A random walk weight calculator calculates cost value, and calculates a weighted value by subtracting the calculated cost value (S730). A filtering part filters the image, by calculating an average among adjacent pixels by using the calculated weighted value (S740). [Reference numerals] (AA) Start; (BB) End; (S710) Divide an image into a block unit; (S720) Calculate cost value; (S730) Calculate weighted value; (S740) Equalize the value of adjacent pixels

Description

Apparatus and Method for Color Image Boundary Clearness

The present invention relates to an image boundary clearing device and method and a recording medium therefor. More specifically, the present invention calculates weights by using a random walk probability model in a bilateral filter and averages the adjacent pixels using the calculated weights to preserve the information on the image boundary while clearing the boundary. The present invention relates to an apparatus and method for clarifying image boundaries and a recording medium therefor.

Recently, as information processing technology for broadcasting and communication is rapidly developed, interest in the next generation broadcasting service is increasing. In this regard, studies on stereoscopic video and multi-view video (MVC) have been conducted to provide realistic three-dimensional stereoscopic images that are differentiated from conventional two-dimensional images. In addition, research on free-viewpoint TV (FTV), which provides an image at any point desired by a user, is also in progress.

In these techniques, a depth image may be effectively used when synthesizing an image, generating a multiview image, or generating an image for an intermediate view. Unlike a color image, a depth image has depth information and contour information of an object, not texture information.

Bilateral filters are used for noise removal and planarization while preserving the boundary of an image. In the case of a general two-sided filter, weights based on color and distance are calculated. The values of adjacent pixels are averaged using the calculated weights. In general, both filters use a method of preserving boundary information by largely calculating weights between close and similar colors.

In the case of a general two-sided filter, there is a problem in that boundaries are not clear for an ambiguous color gamut.

As another bilateral filter method, a boundary between an image and a depth map may be matched to clarify the boundary of the image.

When using this method, the exact depth value actually captured and acquired may be caused by the weighted sum of the surrounding prediction values, and the accuracy of the boundary may not be accurate in the obscure color region around the boundary due to the influence of the initial depth information. There is a problem falling.

The present invention has been made to solve the above problems, and the weight is calculated using the random walk probability, and the reliability of the depth map and the boundary of the image are clearly defined using the calculated weight. It is an object of the present invention to provide an apparatus and method for clarifying image boundaries and a recording medium therefor.

In order to solve the above technical problem, the image boundary disambiguation method according to the present invention is a method for clarifying boundary portions of an image using a bilateral filter, using weights using random walk probability. And a random walk weight calculation step of calculating.

Preferably, the method may further include a filtering step of averaging and filtering values of pixels of an adjacent image by using the weight.

Preferably, the weight using the random walk probability is a value obtained by subtracting the sum of cost values from the pixel at the boundary portion to the pixel at the center portion in the initial probability 1.

Preferably, the cost value is a value obtained by calculating a color difference using a Gaussian distribution between adjacent pixels.

Preferably, the method further includes a block classification step of dividing the entire area of the image into block units having a predetermined size.

The weight calculation may be calculated in units of the divided blocks.

Preferably, the sum of the cost values corresponds to one of a path having the maximum weight or a path having the lowest cost value when two or more paths from the boundary pixel to the center pixel exist. It is characterized in that the selected value calculated.

Preferably, the path having the maximum random walk probability or the path having the smallest cost value is calculated by applying the Dirichlet Problem method.

Preferably, the filtering is performed on a depth map of the image.

In order to solve the above technical problem, the image boundary disambiguation apparatus according to the present invention is a device for clarifying boundary portions of an image using a bilateral filter. The weight is determined using random walk probability. It characterized in that it comprises a random walk weight calculation unit to calculate.

The apparatus may further include a filtering unit which averages and filters values of pixels of an adjacent image by using the weight.

Preferably, the weight using the random walk probability is a value obtained by subtracting the sum of cost values from the pixel at the boundary portion to the pixel at the center portion at the initial probability 1.

Preferably, the cost value is a value obtained by calculating a color difference using a Gaussian distribution between adjacent pixels.

Preferably, the apparatus further includes a block separator for dividing the entire area of the image into block units having a predetermined size, and the weight calculation is performed in the divided block units.

Preferably, the sum of the cost values corresponds to one of a path having the maximum weight or a path having the lowest cost value when two or more paths from the boundary pixel to the center pixel exist. It is characterized in that the selected value calculated.

Preferably, the calculation of the path having the maximum random walk probability or the path having the smallest cost value is calculated by applying a Dirichlet Problem apparatus.

Preferably, the filtering is performed on a depth map of the image.

According to the present invention, weights are calculated using random walk probabilities, and applied to both filters using the calculated weights, thereby improving reliability of a depth map and clarifying image boundaries.

In addition, the present invention can reduce the problem of reducing the overall resolution of the image by generating chromatic aberration of the image.

1 is a block diagram of an image boundary disambiguation apparatus according to a preferred embodiment of the present invention.
FIG. 2 is a diagram for explaining a center pixel and a boundary pixel in an entire area of an image when a weight is calculated in units of blocks according to the present invention.
3 is an exemplary diagram for explaining a cost value in an image boundary disambiguation apparatus according to the present invention.
4 is an exemplary diagram illustrating a process of calculating a weight in an image boundary disambiguation apparatus according to the present invention.
5 is an exemplary view for explaining a method of calculating a cost value when a path is 2 or more in the image boundary disambiguation apparatus according to the present invention.
6 is another exemplary view for explaining a method of calculating a cost value when a path is 2 or more in the image boundary disambiguation apparatus according to the present invention.
7 is a flowchart of an image boundary method according to a preferred embodiment of the present invention.
8 is an exemplary diagram for describing an example in which an image boundary disambiguation method according to the present invention is applied to a depth map.
9 is an exemplary diagram for explaining another example of applying the image boundary disambiguation method according to the present invention to a depth map.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the drawings. In the following description and the accompanying drawings, substantially the same components are denoted by the same reference numerals, and redundant description will be omitted. In the following description of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear.

It is to be understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, . On the other hand, when an element is referred to as being "directly connected" or "directly connected" to another element, it should be understood that there are no other elements in between.

1 is a block diagram of an image boundary disambiguation apparatus according to a preferred embodiment of the present invention.

Referring to FIG. 1, the image boundary disambiguation apparatus 100 includes a random walk weight calculator 120, a block divider 110, and a filter 130.

The random walk weight calculator 120 calculates a weight using a random walk probability.

In detail, the method of calculating the weight using the random walk probability by the random walk weight calculator 120 will be described in comparison with the method of calculating the weight by a general bilateral filter. In the case of a general two-sided filter, weights are calculated using distance and color information between pixels.

Figure pat00001

Referring to Equation 1, a weight calculation method of a general two-side filter will be described. D p is a depth value of the current pixel p. f (b pq) is an absolute value of the distance difference between the pixel at point p and the pixel at point q. g (∥Ip-Iq∥) is a Gaussian color distribution between the pixels. k p is the normalization factor. In general, the two-sided filter using Equation 1 calculates the weights between the similar pixels and the distance between pixels is close, thereby preserving and clarifying the information of the boundary portion.

Unlike the weight calculation method of the general two-sided filter, the random walk weight calculation unit 120 calculates the weight using a random walk probability.

The block separator 110 may block the entire image into a predetermined size.

The image may be a color image including color pixels and may be a depth map image.

The weight may be calculated after dividing the image into block units. In the case of using the two-sided filter by dividing the data into block units and using the two-sided filter, the effect of noise may be reduced more than the case of segmentation by applying random walk probability to the entire image.

FIG. 2 is a diagram for explaining a center pixel and a boundary pixel in an entire area of an image when a weight is calculated in units of blocks according to the present invention.

Figure pat00002

Referring to FIG. 2 and Equation 2, a weight calculation using a random walk probability is described. I is a current pixel and is located at the center of the set of blocks of FIG. 2. j is a boundary pixel. In FIG. 2, the boundary pixel means a pixel of a shaded portion at the edge. n is a repeating number. D n (i) is the pixel value of the block portion located at the center. Dn- 1 (j) is the pixel value of the block located at the boundary portion. That is, Equation 2 refers to the pixel value located at the center of the pixel value of the block located at the boundary.

The weight using the random walk probability is obtained by subtracting the sum of the cost values from the boundary pixel to the central pixel in the initial probability 1. Therefore, the model P (i, j) representing the random walk probability may be expressed as in Equation 3.

Figure pat00003

In Equation 3, w ij represents a cost value from the pixel j of the boundary portion to the pixel i of the center block. k denotes a path, which is determined by pixels i and j. The cost value may be expressed as in Equation 4.

Figure pat00004

3 is an exemplary diagram for explaining a cost value in an image boundary disambiguation apparatus according to the present invention.

Referring to FIG. 3 and Equation 4, the cost value may be a value obtained by calculating a color difference using a Gaussian distribution between adjacent pixels.

In FIG. 3, cost values between pixels i of the central block and adjacent pixels 1, 2, 3, and 4 may be obtained by using Equation 4. In Equation 4, x i -x j means an RGB color difference between the center pixel and the adjacent pixel. The center pixel and the adjacent pixel may have a relative meaning, and x i -x j may mean an RGB color difference between blocks i and j. σ represents the variance in the Gaussian probability distribution and is an adjustable value.

4 is an exemplary diagram illustrating a process of calculating a weight in an image boundary disambiguation apparatus according to the present invention.

In FIG. 4, it is assumed that a cost value of each block calculated according to Equation 4 is w 1 is 0.1, w 2 is 0.05, w 3 is 0.05, and w 4 is 0.1, and the weight is 1- (w 1 + w 2 + w 3 + w 4 ) = 1- (0.1 + 0.05 + 0.05 + 0.1) = 0.7

5 is an exemplary view for explaining a method of calculating a cost value when a path is 2 or more in the image boundary disambiguation apparatus according to the present invention.

6 is another exemplary view for explaining a method of calculating a cost value when a path is 2 or more in the image boundary disambiguation apparatus according to the present invention.

5 and 6, when calculating the sum of the cost values, there may be two or more paths from the boundary pixel to the center pixel. Each path may pass through another block where the cost values may be different so that the sum of the cost values from the same boundary pixel to the same center pixel may be different. If the sum of the cost values is different, the weights are different.

If there are two or more paths, the cost value is calculated by selecting one of the path having the maximum weight using the random walk probability or the path having the minimum cost value. For example, in FIG. 5, if the cost value is 0.1 for the route 1, the cost value is 0.2 for the route 2, and the cost value is 0.15 for the route 3, the cost value of the route 1 is the minimum, and thus the route 1 is selected and weighted. Calculate The weight calculated using the path with the lowest cost value is the maximum.

In FIG. 6, a gray circle is a color pixel, and a black circle is a border pixel. The middle white circle is the current pixel or the center pixel. The center may vary depending on which pixel is viewed as a reference, and the sum of the cost values may be calculated by setting the current pixel as the reference. Calculation of the cost value uses the RGB color difference of adjacent pixels, and FIG. 6 shows that the weight is different because the color pixels passing through the selected path are different when moving from the specific boundary pixel to the center pixel.

In detail, a path having a maximum random walk probability or a path having a minimum cost value may be determined by applying a Dirichlet Problem method. The Dirichlet plamb method is described with reference to Equations 5-8.

Figure pat00005

Figure pat00006

Figure pat00007

Figure pat00008

The Dirichlet plamb method is a method using a Laplace matrix. D [x] represents D n (i) in matrix form. In Equation 5, w ij represents a cost value between pixels, and Σw ij (x i -x j ) 2 can be converted into a matrix operation x T Lx. L is a Laplace matrix that represents the sum of the cost of each path and the cost of each path.

Specifically, the matrix L will be described with reference to Equation 6, i and j each pixel. d i is the sum of the respective path cost values that pixel i has with adjacent pixels. If i and j are equal, then matrix L is di.

If two pixels are close to each other, for example, if | ij | = 1, the matrix L becomes -w ij .

The matrix operations of Equation 5 may be expressed as Equation 7 by representing the elements. L border , x border , or border , means that the row's index in the matrix is the border pixel components, and L non - border , x non - border , or non-border, means the row's index in the matrix. Is the case that is not a boundary pixel component. B is the portion of the entire Laplace matrix that does not belong to L border and L non-border .

The derivative of Equation 7 with respect to X non - border can be derived. Equation 7 is an equation for finding a path in which the sum of cost values becomes the minimum in Equation 6.

As a minimum path solution, a method other than the Dirichlet plamb method may be used. For example, it is also possible to use the Graph-Cut method rather than the Dirichlet plab method, which is a method using Graph theroy.

The filtering unit 130 averages and filters the values of pixels of the adjacent image by using the weights calculated by the random walk weight calculator 120.

That is, the image boundary disambiguation apparatus 100 according to the present invention defines a minimum path for each pixel, defines a probability, and performs a filter using the defined probability.

The image boundary disambiguation apparatus 100 according to the present invention may be applied to an image and may also be applied to a depth map for a 3D image. When the present invention is applied to a depth map, the characteristics of the depth map can be improved.

Specifically, when the image boundary disambiguation device 100 according to the present invention is applied to a color image, a flattened color image in which the boundary is preserved may be obtained.

When the image boundary clearing device 100 according to the present invention is applied to a depth map image, the boundary mismatch problem can be solved and the accuracy of the depth value can be improved.

In addition, the image boundary disambiguation apparatus 100 according to the present invention reduces the sensitivity to noise by calculating a weight using a random walk in units of blocks.

In addition, the image boundary disambiguation apparatus 100 according to the present invention may make the boundary clear even when adjacent pixels have an ambiguous color, thereby obtaining a flattened image in which the boundary is preserved.

7 is a flowchart of an image boundary method according to a preferred embodiment of the present invention.

Referring to FIG. 7, the image boundary method is described. The block divider 110 divides an image into units of a predetermined size block (step S710). The constant size may vary depending on the setting, and may not necessarily be divided into constant sizes, but may be divided into sizes having periodic or aperiodic properties.

The random walk weight calculator 120 calculates a cost value (step S720) and calculates a weight by subtracting the cost value calculated in step 1 (step S730).

The filtering unit 130 calculates and averages the adjacent pixels using the calculated weights (S740).

When the image boundary clearing method according to the present invention is applied to a color image, a flattened color image in which the boundary is preserved can be obtained.

When the image boundary disambiguation method according to the present invention is applied to the depth map image, the boundary mismatch problem can be solved and the accuracy of the depth value can be improved.

In addition, the image boundary disambiguation method according to the present invention reduces the sensitivity to noise by calculating a weight using a random walk on a block basis.

In addition, the image boundary disambiguation method according to the present invention may make the boundary clear even when adjacent pixels have an ambiguous color, thereby obtaining a flattened image in which the boundary is preserved.

8 is an exemplary diagram for describing an example in which an image boundary disambiguation method according to the present invention is applied to a depth map.

Comparing the initial depth map of FIG. 8 with the depth map filtered by the image boundary disambiguation method according to the present invention, it can be seen that the depth map filtered by the image boundary disambiguation method according to the present invention has a clearer boundary.

Unit (dB) Original Depth Map Filtered Depth Map Color image 1 32.1729 32.6592 Color image 2 29.1415 29.1720

Referring to Table 1, in the color image 1 and 2 in FIG. 8, the dB value of the filtered depth map filtered by the image boundary disambiguation method according to the present invention is higher than the dB value of the original depth map. . That is, the characteristics of the depth map filtered by the image boundary disambiguation method according to the present invention are better.

9 is an exemplary diagram for explaining another example of applying the image boundary disambiguation method according to the present invention to a depth map.

9, there is a video image and a depth map is created based on the video image. Compared to the initial depth map, the depth map using the method proposed by the present invention can be seen that the boundary is clearer and the boundary information is preserved.

The image boundary disambiguation method according to the preferred embodiment of the present invention can be embodied as computer readable codes on a computer readable recording medium. A computer-readable recording medium includes all kinds of recording apparatuses in which data that can be read by a computer system is stored.

Examples of the computer-readable recording medium include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage, and the like. Computer-readable code in a distributed fashion can be stored and executed. In addition, functional programs, codes, and code segments for implementing the present invention can be easily deduced by programmers of the art to which the present invention belongs.

It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the disclosed embodiments should be considered in an illustrative rather than a restrictive sense. The scope of the present invention is shown in the claims rather than the foregoing description, and all differences within the scope will be construed as being included in the present invention.

Claims (16)

In a method for clarifying the boundary of an image using a bilateral filter,
And a random walk weight calculation step of calculating a weight using a random walk probability. The method of claim 1,
And a filtering step of averaging and filtering the values of pixels of an adjacent image by using the weights. The method of claim 1,
The weight using the random walk probability is an initial probability of 1, which is a value obtained by subtracting a sum of cost values from a pixel at a boundary to a pixel at a central portion. The method of claim 3, wherein
And the cost value is a value obtained by calculating a color difference using a Gaussian distribution between adjacent pixels. The method of claim 1,
Block dividing step of dividing the entire area of the image by a block unit of a predetermined size,
The weight calculation is an image boundary disambiguation method, characterized in that calculated in each of the divided block units. The method of claim 3, wherein
The sum of the cost values is calculated by selecting one of the path having the maximum weight or the path having the lowest cost value when there are two or more paths from the boundary pixel to the center pixel. Image boundary disambiguation method, characterized in that the value. The method according to claim 6,
The method of claim 1, wherein the path having the largest random walk probability or the path having the smallest cost value is calculated by applying a Dirichlet Problem method. 3. The method of claim 2,
And the filtering is performed on a depth map of the image. In the device for clarifying the boundary of the image using a bilateral filter,
And a random walk weight calculator configured to calculate a weight using a random walk probability. The method of claim 9,
And a filtering unit which averages and filters values of pixels of an adjacent image by using the weights. The method of claim 9,
And the weight using the random walk probability is a value obtained by subtracting a sum of cost values from the pixel at the boundary to the pixel at the center at an initial probability 1. The method of claim 11,
And the cost value is a value obtained by calculating a color difference using a Gaussian distribution between adjacent pixels. The method of claim 9,
Further comprising a block divider for dividing the entire area of the image by a block unit of a predetermined size,
And calculating the weights, respectively, in each of the divided block units. The method of claim 11,
The sum of the cost values is calculated by selecting one of the path having the maximum weight or the path having the lowest cost value when there are two or more paths from the boundary pixel to the center pixel. Image boundary disambiguation device, characterized in that the value. 15. The method of claim 14,
And calculating a path having a maximum random walk probability or a path having a minimum cost value by calculating a Dirichlet Problem device. 11. The method of claim 10,
And the filtering is performed on a depth map of the image.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113545071A (en) * 2019-03-12 2021-10-22 Kddi 株式会社 Image decoding device, image decoding method, and program

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113545071A (en) * 2019-03-12 2021-10-22 Kddi 株式会社 Image decoding device, image decoding method, and program

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