AN IMPROVED SYNTHETIC APERTURE RADAR IMAGE DENOISING METHOD BASED ON BLOCK-MATCHING AND 3D FILTERING

Abstract. Block-matching and 3D filtering (BM3D) are used to reduce the multiplicative coherent speckle noise of Synthetic Aperture Radar (SAR) images, which may lead to the loss of image details. This paper proposes an improved similarity metric BM3D algorithm. Firstly, this method analyses the coherent speckle noise model, and applies a logarithmic transformation to make the BM3D algorithm suitable for multiplicative noise. Secondly, this method based on the calculation method of Euclidean distance weights for similar image blocks, and the Pearson correlation coefficient is introduced to improve the similarity metric. The accuracy of similar image block matching is improved, which is beneficial for removing image noise and maintaining image information. The experiments in this paper compared the results of this method with Frost filtering, Kuan filtering, wavelet soft thresholding and SAR-BM3D filtering algorithms. The results were compared and analysed by subjective vision and objective indicators. The experimental results show that compared with other filtering algorithms, the proposed algorithm has better ability to reduce speckle noise and preserve edge detail information for the image.