Patch-based Contour Prior Image Denoising for Salt and Pepper Noise
This addresses image denoising for applications affected by salt and pepper noise, but it is incremental as it builds on existing patch-based and contour prior techniques.
The paper tackles the problem of removing salt and pepper noise from images while preserving details, proposing a patch-based contour prior denoising method that achieves competitive results with state-of-the-art methods in terms of PSNR and visual effects.
The salt and pepper noise brings a significant challenge to image denoising technology, i.e. how to removal the noise clearly and retain the details effectively? In this paper, we propose a patch-based contour prior denoising approach for salt and pepper noise. First, noisy image is cut into patches as basic representation unit, a discrete total variation model is designed to extract contour structures; Second, a weighted Euclidean distance is designed to search the most similar patches, then, corresponding contour stencils are extracted from these similar patches; At the last, we build filter from contour stencils in the framework of regression. Numerical results illustrate that the proposed method is competitive with the state-of-the-art methods in terms of the peak signal-to-noise (PSNR) and visual effects.