Abstract
Salt and Pepper noise (S&P noise) removal is an active research area in digital image processing. Existing techniques commonly use the local statistics within a neighborhood to estimate the centered noisy pixel, and tend to damage image details due to the image local diversity singularity and non-stationarity. To address this problem, in this paper, iterative nonlocal means filter (INLM) is proposed to exploit the image non-local similarity feature in the S&P noise removal procedure. Moreover, the proposed iterative framework update the similarity weights and the estimated values for higher accuracy. The experimental results show that the proposed INLM produces better results than state-of-art methods over a wide range of scenes both subjectively and objectively, and it is robust to the detection results.
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