External Patch Prior Guided Internal Clustering for Image Denoising

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Authors Fei Chen, Lei Zhang, and Huimin Yu
Journal/Conference Name 2015 IEEE International Conference on Computer Vision (ICCV 2015)
Paper Category
Paper Abstract Natural image modeling plays a key role in many vision problems such as image denoising. Image priors are widely used to regularize the denoising process, which is an ill-posed inverse problem. One category of denoising methods exploit the priors (e.g., TV, sparsity) learned from external clean images to reconstruct the given noisy image, while another category of methods exploit the internal prior (e.g., self-similarity) to reconstruct the latent image. Though the internal prior based methods have achieved impressive de-noising results, the improvement of visual quality will be-come very difficult with the increase of noise level. In this paper, we propose to exploit image external patch prior and internal self-similarity prior jointly, and develop an external patch prior guided internal clustering algorithm for image denoising. It is known that natural image patches form multiple subspaces. By utilizing Gaussian mixture models(GMMs) learning, image similar patches can be clustered and the subspaces can be learned. The learned GMMs from clean images are then used to guide the clustering of noisy-patches of the input noisy images, followed by a low-rank approximation process to estimate the latent subspace for image recovery. Numerical experiments show that the pro-posed method outperforms many state-of-the-art denoising algorithms such as BM3D and WNNM.
Date of publication 2015
Code Programming Language MATLAB
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