Non-mean filtering method and non-mean filtering device

Abstract

The invention discloses a non-mean filtering method and a non-mean filtering device, which realize the controllable intensity of the non-local mean filtering of a CFA image and avoid the resource consumption in the exponential operation process of Gaussian function mapping. The method comprises the following steps: determining a central block and at least one search block of a color filter array CFA image; obtaining a gray value of each pixel point in the central block and the current search block, and dividing the gray value into a smooth part and an unsmooth part; performing calculating to obtain a difference value between the smooth parts of the central block and the current search block; performing calculating to obtain a sum SAD value of absolute value differences of corresponding points of the central block and the unsmoothed part of the current search block; obtaining a search address according to the difference value and the SAD value; obtaining a weight value of the current search block from a preset lookup table according to the lookup address, wherein the lookup address and the weight value in the preset lookup table accord with a Gaussian curve; and performing calculating to obtain a filtering result of the CFA image according to the gray values and the weight values of the central pixel points of the central block and all the search blocks.

Description

technical field [0001] The invention relates to the field of digital image processing, in particular to a non-average filtering method and a non-average filtering device. Background technique [0002] Non-local-mean (NLM) technology uses the current point as the center to search for similar blocks around, and assigns different weights according to the degree of similarity. The gray value of the current point is obtained by the weighted average of the surrounding points . [0003] Compared with the mean filter, NLM can protect the edge of the image while filtering out the noise to a certain extent, so that the picture will not be too blurred. Generally, the weight of the center point of the search block needs to be obtained through Gaussian function projection, but the Gaussian function involves exponential operations for hardware implementation, the algorithm complexity is high, and the required hardware resource overhead is also large. Contents of the invention [0004]...

AI Technical Summary

Problems solved by technology

Generally, the weight of the center point of the search block needs to be obtained through the Gaussian function projection, but the Gaussian function involves exponential operations for hardware implementation, the algorithm complexity is high, and the required hardware resource overhead is also large

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