Zhiqin Zhao, Liang Luo, Journal of Information Processing Systems Vol. 22, No. 1, pp. 62-74, Feb. 2026  

https://doi.org/10.3745/JIPS.01.0116
Keywords: Image denoising, Low Rank Matrix Approximation, Markov-Chain Monte Carlo, Slice Sampling
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Abstract

A new method for non-local random denoising using slice sampling is introduced. This method can significantly enhance the efficiency of non-local image denoising algorithms. The proposed algorithm consists of two stages: first, similar image patches are searched using slice sampling, and then a denoising algorithm is designed to reconstruct the original image using these similar patches. Low-rank matrix approximation methods are used to obtain estimates of clean patches, and a clean denoised image is generated through superposition. The theoretical analysis and experimental tests demonstrate that this algorithm can overcome the dependence on proposal distributions in traditional random algorithms. The experimental results on benchmark images with additive Gaussian noise show that the proposed method can achieve good performance compared to state-of-the-art methods such as BM3D. Specifically, for the test image “Lena” with a noise standard deviation of 20, this method can achieve a peak signal-to-noise ratio (PSNR) of 32.82 dB and a structural similarity index measure (SSIM) of 0.87. For the “Barbara” image with the same noise level, the PSNR is 31.50 dB and the SSIM is 0.89. These results confirm the effectiveness of the algorithm in denoising performance and edge preservation.

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