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Learning Non-Local Range Markov Random Field for Image Restoration

8 years 2 months ago
Learning Non-Local Range Markov Random Field for Image Restoration
In this paper, we design a novel MRF framework which is called Non-Local Range Markov Random Field (NLRMRF). The local spatial range of clique in traditional MRF is extended to the non-local range which is defined over the local patch and also its similar patches in a non-local window. Then the traditional local spatial filter is extended to the non-local range filter that convolves an image over the non-local ranges of pixels. In this framework, we propose a gradient-based discriminative learning method to learn the potential functions and non-local range filter bank. As the gradients of loss function with respect to model parameters are explicitly computed, efficient gradient-based optimization methods are utilized to train the proposed model. We implement this framework for image denoising and inpainting, the results show that the learned NLR-MRF model significantly outperforms the traditional MRF models and produces state-of-the-art results.
Sun Jian, Marshall Tappen
Added 08 Apr 2011
Updated 29 Apr 2011
Type Journal
Year 2011
Where CVPR
Authors Sun Jian, Marshall Tappen
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