A differential evolution BM3D hard threshold parameter denoising method for remote sensing images

doi:10.13474/j.cnki.11-2246.2023.0043

Abstract

Abstract: Aiming at the problem of denoising of Gaussian white noise in remote sensing images, a differential evolution (DE) algorithm is used to optimize the hard threshold transformation parameters in the 3D block matching (BM3D) algorithm, which includes the block distance threshold and the 3D transformation domain hard threshold parameter, and then the optimized BM3D algorithm is used to eliminate Gaussian white noise in the image. Taking peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and edge preservation index (EPI) as the evaluation indicators, the experimental results show that the fusion algorithm has better performance in PSNR, SSIM and EPI under different noise densities, especially the EPI has increased by about 2%. On the whole, the denoising effect of the remote sensing image Gaussian white noise of the fusion algorithm is better than that of the single BM3D denoising algorithm.

Key words: 3D block matching algorithm, differential evolution, hard threshold parameter, Gaussian white noise denoising, fusion

CLC Number:

P237

HU Pengcheng, TANG Shihua, ZHANG Yan, LIU Kunzhi, Lü Fuqiang, LI Haoyang. A differential evolution BM3D hard threshold parameter denoising method for remote sensing images[J]. Bulletin of Surveying and Mapping, 2023, 0(2): 72-77.

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