KR100617651B1 - Apparatus and method for removing grid noise - Google Patents

Abstract

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a lattice noise removing device and a method, and to calculate an average value of each line of an input Bayer pattern image, an even line disposed between odd lines of a Bayer pattern image, and an odd line disposed between even lines. After calculating an estimated value for estimating the average of the obtained value, a correction coefficient is obtained using the difference between the estimated value and the average value, and then the correction coefficient is calculated on the Bayer pattern image to output the corrected Bayer pattern image. According to the present invention, the plaid noise can be removed without damaging the details.

Plaid, Noise, Bayer Pattern Image, Estimation, Average, Correction Factor

Description

Apparatus and method for removing grid noise}

1 is an exemplary diagram of average values of green component pixels in the horizontal direction of a typical Bayer pattern image;

2 is an exemplary diagram for describing a grid noise generated by color interpolation in a conventional image processing system;

3 is an exemplary view for explaining a mask of a Bayer pattern image input to the removing device of the present invention;

4 is a structural diagram of an embodiment of a plaid noise removing device according to the present invention;

5 is an exemplary view for explaining a correction coefficient generated by the correction coefficient generator of FIG. 1;

6 is an exemplary view for explaining a correction performed by the correction unit of FIG. 1;

7A is an exemplary graph illustrating average values of pixels of a green component in a horizontal direction after a plaid noise is controlled by the present invention;

7B is an enlarged partial view of FIG. 7A;

8 is an example of an image in which a plaid noise is removed according to the present invention;

9 is an exemplary view for explaining that the correction coefficient obtained in accordance with the present invention is optimized;

10 is an exemplary view for explaining a correction coefficient obtained from a 6x6 mask according to the present invention.

<Explanation of symbols for the main parts of the drawings>

410: line average calculation unit 420: line estimation unit

430: difference calculation unit 440: correction coefficient generation unit

450: correction unit

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an apparatus and method for removing plaid noise, and more particularly, to an apparatus and method for eliminating plaid noise for use in an image processing system.

In general, a Bayer pattern image input to an image processing system is interpolated by a difference between Gr (green adjacent to red) and Gb (green adjacent to blue) line values. Color interpolation produces grid noise. This will be described with reference to the drawings.

1 is an exemplary diagram of an average value of green component pixels in a horizontal direction of a general Bayer pattern image.

As shown in the figure, when looking at the average value of the green component pixels in the horizontal direction in the Bayer pattern image, it can be seen that the difference in the value between the even-numbered lines and the odd-numbered lines is quite severe. This difference in value causes the noise of the lattice pattern as shown in FIG. 2 through color interpolation. FIG. 2 is an exemplary diagram for describing lattice noise caused by color interpolation in a conventional image processing system.

In order to remove such a plaid noise, a filter such as a Gaussian filter or a median filter is mainly used in the related art, but in the case of using the filter, the plaid noise can be removed, but the image There is a problem of damaging the details (i.e., edges or boundaries with high frequency components, etc.) together. That is, the method of removing the plaid noise using a conventional filter has a problem that it is practically impossible to remove the plaid noise while maintaining the detail of the image.

The present invention has been proposed to solve the above problems, and when the center pixel in the mask is a green component, it is possible to remove the grid noise without damaging the image by using the difference between the average values of the pixels in the even and odd lines. It is an object of the present invention to provide an apparatus and method for removing a plaid noise.

In order to achieve the above object, according to a preferred embodiment of the present invention, an average value calculation unit for calculating the average value of each line of the input Bayer pattern image; An estimator for calculating an estimated value for estimating an average of even lines disposed between odd lines of the Bayer pattern image and odd lines disposed between even lines; A difference calculator for calculating a difference value between the estimated value calculated by the estimator and the average value calculated by the average value calculator; A grid noise removing device including a correction coefficient calculator for calculating a correction coefficient using a difference value generated by the difference calculator is provided.

At this time, it is preferable to further include a correction unit for outputting the corrected Bayer pattern image by calculating the correction coefficient to the Bayer pattern image.

Preferably, the correction coefficient obtained by the correction coefficient calculating unit obtains an average of the difference values calculated by the difference calculating unit and halves the average, and the correction coefficient may be determined for each green component of the Bayer pattern image. Can be.

On the other hand, the correction unit performs a difference operation of the correction coefficient of each green component for each green component of the Bayer pattern image for odd lines, and corrects each green component for each green component of the Bayer pattern image for even lines Perform the sum operation of the coefficients.

Further, according to another embodiment of the invention, the step of calculating the average value of each line of the input Bayer pattern image; Calculating an estimated value for estimating an average of even lines disposed between odd lines of the Bayer pattern image and odd lines disposed between the even lines; Calculating a difference value between the estimated value and the average value; Calculating a correction coefficient using the difference value; And calculating the Bayer pattern image by outputting the corrected coefficient to the Bayer pattern image.

The above objects, features and advantages will become more apparent from the following detailed description taken in conjunction with the accompanying drawings. First of all, in adding reference numerals to the components of each drawing, it should be noted that the same components have the same number as much as possible even if displayed on different drawings. Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.

FIG. 3 is an exemplary view for explaining a mask of a Bayer pattern image input to the removing device of the present invention. The present invention is applied when the center pixel in the 5 × 5 mask as shown in FIG. 3 is green. However, for convenience of description, the 5 × 5 mask that is most used in image processing will be described as an example, but is not limited thereto.

Figure 4 is a structural diagram of an embodiment of a lattice noise removing device according to the present invention.

As shown in the figure, the removal apparatus of the present invention, including a line average value calculator 410, a line estimator 420, the difference calculator 430, a correction coefficient generator 440 and a correction unit 450 It is composed.

The line average calculator 410 is responsible for calculating an average value of each line of the Bayer pattern image input as shown in FIG. 3. The average value of each line is shown in Equations 1 to 5, respectively.

Using the average value of each line thus obtained, the line estimating unit 420 is responsible for estimating the average value of the lines arranged between the lines. That is, the second line is estimated using the average value of the pixels for the first and the third line, the third line is estimated using the average value of the pixels for the second and the fourth line, and the third and fifth It is responsible for estimating the fourth line using the average value of the pixels with respect to the line. This is because an even line has no information about an odd line (or an odd line has no information about an even line), so a difference in the average value as shown in FIG. 1 occurs. .

When the average of the difference between the estimated value and the line average value calculation unit 410 calculated as described above is 1/2, the value corresponds to 1/2 of the interval between the even and odd lines of FIG. 1. will be. This will be described later.

The estimated values of the second, third and fourth lines estimated by the line estimator 420 are as follows. The line estimator 420 estimates and performs the average of two lines.

The difference calculator 430 of FIG. 1 is responsible for calculating a difference between the average value obtained by the line average calculator 410 and the estimated value obtained by the line estimator 420. That is, the difference between the graph of the even lines and the odd lines of FIG. 1 is obtained. Therefore, the difference calculated by the difference calculating unit 430 is as follows.

In this case, calculating '(estimated value)-(average value)' for even lines, and calculating '(average value)-(estimated value)' for odd lines, the average value of even lines is generally smaller than the estimated value in FIG. This is because the average value of the odd line has a larger value than the estimated value, and the subtraction operation is performed on the odd line, and the addition operation is applied to the even line when the correction factor is applied later.

The correction coefficient generator 440 of FIG. 4 is responsible for generating a correction coefficient α to be applied to the Bayer pattern image by using the difference obtained in Equations 9 to 11. This is equal to 1/2 of the average value of Equations 9 to 11. That is, the following equation (12).

This is shown in FIG. 5. 5 is an exemplary diagram for describing a correction coefficient generated by the correction coefficient generator of FIG. 1. As shown in the figure, a correction coefficient may be generated by multiplying a 5 × 5 mask of the Bayer pattern of FIG. 3 by a constant. Here '. ×' means the product of elements in the same position.

The correction unit 450 is responsible for removing the grid noise by applying the correction coefficient α calculated as described above to the input Bayer pattern image. That is, the finally obtained correction coefficient α is responsible for adding or subtracting the green component of the Bayer pattern of FIG. This will be described with reference to FIG. 6.

6 is an exemplary view for explaining a correction performed by the correction unit of FIG. 1.

As shown in the figure, the correction coefficient α obtained by the correction coefficient generation unit is added to or subtracted from the green component of the Bayer pattern image. The subtraction operation is performed on odd lines when the correction factor is applied, and the addition operation is applied to even lines as described above.

FIG. 7A is an exemplary graph illustrating average values of pixels of a green component in the horizontal direction after the grid noise is controlled by the present invention, and FIG. 7B is an enlarged view of a portion of FIG. It is enlarged.

As shown in the figure, when compared with FIG. 1, it can be seen that the average value of the pixels in the horizontal direction is reduced.

8 is an example of an image from which the grid noise is removed according to the present invention. As shown in the figure, it can be seen that the grid noise is significantly removed compared to the prior art of FIG.

FIG. 9 is an exemplary view illustrating an optimization of a correction coefficient obtained according to the present invention. The correction coefficient α obtained through the above process is optimized for a system.

In the above, the 5x5 mask has been described as an example, but the present invention is not limited thereto.

10 is an exemplary view for explaining a correction coefficient obtained from a 6x6 mask according to the present invention.

As shown in the figure, the grid noise removing apparatus of the present invention can remove the grid noise by obtaining a correction coefficient for the N × N mask and adding it to the green component of the input image.

The present invention described above is not limited to the above-described embodiments and the accompanying drawings, and various substitutions, modifications, and changes are possible in the art without departing from the technical spirit of the present invention. It will be clear to those of ordinary knowledge.

As described above, the present invention improves the problem of deteriorating image quality by damaging fine parts (edges or boundaries with many high frequency components etc.), although the method using a filter can remove the grid noise. For example, while maintaining the characteristics of the pixels of the even and odd lines, it is possible to remove the lattice noise without damaging the minute parts by correcting the difference between the two lines and adjusting them on the same line.

Claims (9)

An average value calculator for calculating an average value of each line of the input Bayer pattern image; An estimator for calculating an estimated value for estimating an average of even lines disposed between odd lines of the Bayer pattern image and odd lines disposed between even lines; A difference calculator for calculating a difference value between the estimated value calculated by the estimator and the average value calculated by the average value calculator; And And a correction coefficient calculator for calculating a correction coefficient by using the difference value generated by the difference calculator. The apparatus of claim 1, further comprising a correction unit configured to calculate the correction coefficient to the Bayer pattern image and output a corrected Bayer pattern image. The apparatus of claim 1, wherein the difference calculating unit performs a difference operation of the average value with respect to the estimated value with respect to an even line. The apparatus of claim 1, wherein the difference calculating unit performs a difference operation of the estimated value with respect to the average value with respect to an odd line. The apparatus of claim 1, wherein the correction coefficient obtained by the correction coefficient calculating unit obtains an average of the difference values calculated by the difference calculating unit and halves the average. The apparatus of claim 1 or 5, wherein the correction coefficient is determined for each green component of the Bayer pattern image. The apparatus of claim 1, wherein the correction unit performs a difference operation of a correction coefficient of each green component with respect to each green component of the Bayer pattern image with respect to an odd line. 2. The apparatus of claim 1, wherein the correcting unit performs a sum operation of a correction coefficient of each green component with respect to each green component of the Bayer pattern image with respect to an even line. Calculating an average value of each line of the input Bayer pattern image; Calculating an estimated value for estimating an average of even lines disposed between odd lines of the Bayer pattern image and odd lines disposed between the even lines; Calculating a difference value between the estimated value and the average value; Calculating a correction coefficient using the difference value; And And calculating the Bayer pattern image to output the corrected Bayer pattern image.

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