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- W2918258826 abstract "Noise-aided stochastic resonance has been explored in recent literature as a powerful tool that enhances the performance of non-linear systems, particularly in image enhancement and image watermarking. In this paper, we extend the application of stochastic resonance to improve the performance of the conventional non-local means (NLM) filtering for edge-preserving image denoising. The NLM algorithm typically involves computation of weights denoting similarity of a pixel with all other pixels in the image. In the proposed algorithm, these similarity weights are iteratively processed using the concept of dynamic stochastic resonance. The results indicate a significant improvement in sharpness of edges in the denoised images in comparison with the conventional NLM approach both visually and quantitatively in terms of full-reference and no-reference image quality metrics." @default.
- W2918258826 created "2019-03-11" @default.
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- W2918258826 date "2018-11-01" @default.
- W2918258826 modified "2023-10-16" @default.
- W2918258826 title "NOISE-AIDED EDGE-PRESERVING IMAGE DENOISING USING NON-LOCAL MEANS WITH STOCHASTIC RESONANCE" @default.
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- W2918258826 doi "https://doi.org/10.1109/globalsip.2018.8646493" @default.
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