Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/67514
Title: External patch prior guided internal clustering for image denoising
Authors: Chen, F
Zhang, L 
Yu, H
Issue Date: 2015
Source: 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 7-13 Dec 2015, p.603-611
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 denoising results, the improvement of visual quality will become 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 proposed method outperforms many state-of-the-art denoising algorithms such as BM3D and WNNM.
Keywords: Image denoising
Noise reduction
Covariance matrices
Noise measurement
Image reconstruction
Minimization
Euclidean distance
Publisher: Institute of Electrical and Electronics Engineers
ISBN: 978-1-4673-8391-2 (electronic)
978-1-4673-8390-5 (USB)
EISSN: 2380-7504
DOI: 10.1109/ICCV.2015.76
Appears in Collections:Conference Paper

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