Patch-based Contour Prior Image Denoising for Salt and Pepper Noise
Bo Fu, XiaoYang Zhao, Yi Li, XiangHai Wang

TL;DR
This paper introduces a patch-based contour prior method for effectively removing salt and pepper noise from images while preserving details, utilizing contour structures and regression filtering.
Contribution
It proposes a novel patch-based denoising approach that leverages contour priors and regression filtering to improve salt and pepper noise removal.
Findings
Achieves competitive PSNR compared to state-of-the-art methods.
Preserves image details effectively during denoising.
Demonstrates good visual quality in noisy image restoration.
Abstract
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsImage and Signal Denoising Methods · Advanced Image Fusion Techniques · Advanced Image Processing Techniques
