Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising

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MATLAB code that implements the Patch Group Prior Denoising (PGPD) method for image denoising as described in the following paper: “Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising”.

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Authors Jun Xu, Lei Zhang, Wangmeng Zuo, David Zhang, and Xiangchu Feng
Journal/Conference Name 2015 IEEE International Conference on Computer Vision (ICCV 2015)
Paper Category
Paper Abstract Patch based image modeling has achieved a great success in low level vision such as image denoising. In particular, the use of image nonlocal self-similarity (NSS) prior, which refers to the fact that a local patch often has many nonlocal similar patches to it across the image, has significantly enhanced the denoising performance. However, inmost existing methods only the NSS of input degraded image is exploited, while how to utilize the NSS of clean natural images is still an open problem. In this paper, we pro-pose a patch group (PG) based NSS prior learning scheme to learn explicit NSS models from natural images for high-performance denoising. PGs are extracted from training images by putting nonlocal similar patches into groups, and a PG based Gaussian Mixture Model (PG-GMM) learning algorithm is developed to learn the NSS prior. We demonstrate that, owe to the learned PG-GMM, a simple weighted sparse coding model, which has a closed-form solution, can be used to perform image denoising effectively, resulting in high PSNR measure, fast speed, and particularly the best visual quality among all competing methods.
Date of publication 2015
Code Programming Language MATLAB

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