Image filtering method and device

Abstract

The invention provides an image filtering method and device, and the method comprises the steps: selecting a pixel point from a to-be-filtered image as a to-be-filtered point; selecting a matrix withthe size of (2N + 1) * (2N + 1) as a matrix to be filtered by taking the point to be filtered as a center, where N is a positive integer; selecting (2N + 1) pieces of data from the to-be-filtered matrix in the 0-degree direction, the 45-degree direction, the 90-degree direction and the 135-degree direction; performing directional matching on the 0-degree direction, the 45-degree direction, the 90-degree direction and the 135-degree direction by utilizing the selected (2N + 1) * 4 groups of data so as to obtain filtering output values in the four directions through calculation respectively; andaccording to the filtering output values in the four directions, calculating to obtain a filtering output value of the to-be-filtered point. According to the embodiment of the invention, the problemsof image detail loss and large calculation amount in the existing filtering method are solved, the edge details of the image are more truly reserved while the noise is effectively filtered out, and meanwhile, the calculation amount is reduced.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to an image filtering method and device. Background technique [0002] Image filtering is a very important part of image processing. At present, commonly used spatial filtering methods include: mean filtering, median filtering, etc. There is another commonly used filtering method: non-local mean filtering, which is based on block matching. The method mainly uses the redundancy of the image for filtering. [0003] Among them, the above-mentioned non-local mean filtering is: for the filtering of each pixel point, first determine a matching block centered on the pixel point, then determine a search area, use the matching block to traverse the entire search block in turn, and calculate the matching block and the search block The Gaussian weighted Euclidean distance to get the final weight. Finally, the weighted calculation is carried out with this weight to obtain the...

AI Technical Summary

Problems solved by technology

[0005]1) Mean filtering, although this filtering method can better remove Gaussian noise, but it is easy to blur the image and lose the details of the image

[0006]2) Median filtering can remove salt and pepper noise well, but the processing effect on Gaussian noise is not very ideal

[0007]3) Although the non-local mean filtering method can obtain better visual effects, only a unidirectional search of a 3x3 matching block requires only a hundred multiplication operations. However, the matching block and the search area are too small to achieve good results. Therefore, if you want to achieve a better filtering effect, a large amount of calculation is required, and it is not easy to implement in hardware.

[0008] For the above problems, no effective solutions have been proposed so far

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