Progressive Image Denoising Algorithm

doi:10.16182/j.issn1004731x.joss.201702007

Abstract

Abstract: Currently almost all denoising algorithms are implemented by processing original noisy image itself simply, which could not enhance the performance further by combining original noisy image with the denoised image. To solve the problem, a framework of progressive image denoising method was proposed. The framework is based on the block matching and 3D collaborative filtering (BM3D) algorithm, which has the most remarkable denoising effect. It includes three layers and two fusions. Each layer is implemented by BM3D and denoises the fused image generated from the previous layers. Adequate statistical results show that under the same noise condition, our proposed method and another new algorithm can improve original BM3D on PSNR to different degrees, but ours has a better performance. As the noise increases, the performance improvement is more remarkable, which means that the proposed method can improve CT imaging quality and obtain good results.

Key words: block matching and 3D collaborative filtering, non-local similarity, image fusion, progressive

CLC Number:

TP391.9

Li Haiyang, Cao Weiguo, Li Shirui, Tao Kelu, Li Hua. Progressive Image Denoising Algorithm[J]. Journal of System Simulation, 2017, 29(2): 282-294.

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