SAR image de-speckling method based on improved Bayes non-local mean filter

Abstract

The invention discloses an improved Bayes non-local mean filter used in a synthetic aperture radar (SAR) image de-speckling method, which belongs to the technical field of image processing and mainly overcomes the problems of compressed brightness of strong reflection targets such as points, edges and the like and unsatisfied mean maintenance and the like in a de-speckling result of the original Bayes non-local mean filter. The method is implemented by the following steps: (1) performing mean pre-evaluation on an input SAR image v to obtain a pre-evaluated mean matrix u; (2) pre-selecting blocks in a search area at the xi position of a pixel point in the input SAR image, and marking the result as a block set delta 0; (3) pre-selecting points on elements in the block set delta 0, and marking the result as a point set delta; and (4) adopting the Bayes non-local mean filter on each pixel point in the input SAR image to obtain de-speckling image by using the mean matrix u and the point set delta. The method can realize mean and texture maintenance, better keep the brightness of strong reflection targets such as points, edges and the like and is favorable to point target and edge detection.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a filter which can be used for speckle removal processing on SAR images. Background technique [0002] The image formed by synthetic aperture radar SAR has the characteristics of all-weather, all-time, high resolution and strong penetrating ability. Therefore, this image is widely used in target recognition, transformation detection and water surface surveillance. However, SAR images are corrupted by multiplicative noise, which comes from continuous interference from backscatter radar reflections. This speckle noise destroys the radiometric resolution of SAR images and affects the performance of background analysis and understanding tasks. [0003] The goal of despeckling methods is to preserve image feature information, such as textures, edges and point objects, while removing noise. But this goal is very difficult to achieve due to the multiplicative background of sp...

AI Technical Summary

Problems solved by technology

However, the transform domain despeckling algorithm is still essentially a filter based on a fixed window, and Gibbs phenomenon will occur in areas such as edges and lines of the image.

[0004] Although the method based on Bayesian non-local mean filtering achieves both edges and smooth areas to a certain extent, it largely compresses the brightness of strong reflection targets such as points. There is also a large deviation in

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