Improvement of BM3D Algorithm and Employment to Satellite and CFA Images Denoising

TL;DR

This paper enhances the BM3D denoising algorithm by adapting parameters based on noise levels, removing prefiltering, and extending its application to satellite and CFA images, resulting in improved PSNR, visual quality, and reduced processing time.

Contribution

It introduces a noise-level adaptive modification to BM3D and extends its application to satellite and CFA images, outperforming existing methods.

Findings

Improved PSNR and visual quality in denoising results.

Reduced processing time compared to original BM3D.

Superior performance over Adaptive PCA in denoising CFA images.

Abstract

This paper proposes a new procedure in order to improve the performance of block matching and 3-D filtering (BM3D) image denoising algorithm. It is demonstrated that it is possible to achieve a better performance than that of BM3D algorithm in a variety of noise levels. This method changes BM3D algorithm parameter values according to noise level, removes prefiltering, which is used in high noise level; therefore Peak Signal-to-Noise Ratio (PSNR) and visual quality get improved, and BM3D complexities and processing time are reduced. This improved BM3D algorithm is extended and used to denoise satellite and color filter array (CFA) images. Output results show that the performance has upgraded in comparison with current methods of denoising satellite and CFA images. In this regard this algorithm is compared with Adaptive PCA algorithm, that has led to superior performance for denoising CFA images, on the subject of PSNR and visual quality. Also the processing time has decreased significantly.