A synthetic aperture radar image non-local mean filter method

Abstract

The invention provides a synthetic aperture radar image non-local mean filter method, belonging to the radar image processing field. The invention introduces the heterogeneous property measure into the non-local average algorithm, and improves the weight measurement mode of the non-local average algorithm. Firstly, the traditional non-local mean method is weighted by equal weights in calculating the distance measure. In this paper, variogram coefficients are introduced to calculate the weighted weights of the distance measure of pixels in the window of an image block. Because the variogram coefficient can represent the texture direction of the image, the similarity of the image blocks weighted by the variogram coefficient can capture the image blocks with similarity in texture and direction more effectively, thus avoiding the blurring of edge and direction. Then, an adaptive degeneration parameter function based on variogram coefficients is designed. Adaptive adjustment of degenerationparameters can effectively ensure that the heterogeneous regions are not over-smoothed. Therefore, the invention can more effectively keep the texture edge information while filtering out the coherent speckles.

Description

technical field [0001] The invention belongs to the field of radar image processing, in particular to a non-local mean value filtering method of a synthetic aperture radar image. Background technique [0002] Synthetic Aperture Radar (SAR) is an active high-resolution imaging sensor that can observe all-weather and all-weather, and has strong penetration. It is widely used in military reconnaissance, disaster monitoring, and surface cover detection. and other fields. However, because SAR imaging uses echo coherent superposition for imaging, coherent speckle noise inevitably exists in SAR images. These coherent speckle noises reduce the visual effect of SAR images and bring difficulties to subsequent image segmentation and target recognition. Therefore, the speckle suppression of SAR images is of great significance to the segmentation, classification and recognition of SAR images. [0003] Speckle filtering methods for SAR images can be divided into spatial domain filtering...

AI Technical Summary

Problems solved by technology

However, when these methods calculate the distance measure, they use the mean value of the gray distance of the pixels in the window, or the Gaussian weighted value, without considering the texture direction information of the image, and there is a large amount of texture direction information in the SAR image. This information should be an important basis for distance measurement

In addition, in the NLM algorithm, the weight is a Gaussian kernel function that depends on the distance measure, and the degradation parameter has a very important influence on the filtering effect. However, there are few literatures on the degradation parameter, which are generally set according to the empirical value. Does not achieve the effect of adaptive denoising

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