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Unni, VS and Chaudhury, Kunal N (2018) NON-LOCAL PATCH-BASED REGULARIZATION FOR IMAGE RESTORATION. In: 5th IEEE International Conference on Image Processing (ICIP), pp. 1108-1112.

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Official URL: https://doi.org/10.1109/ICIP.2018.8451473


Several patch-based models have been proposed for image restoration in the literature. A common feature with these models is that patches are used for filtering or optimization, where the aggregation is often performed over a non-local (NL) neighborhood. We propose a NL patch-based regularizer, where patches are used (1) for computing weights between NL neighbors, and (2) for defining a TV-type norm in patch space. A general form of the latter construction was originally proposed by Peyre et al. and later studied by other authors. In most of these proposals, both the weights and the image are treated as variables, which makes the model non-convex. In particular, the corresponding numerical solvers cannot guarantee local optimality. On the other hand, our regularizer is convex. Along with an l(2) data fidelity term, we apply the regularizer for denoising, deblurring and super-resolution, and develop an efficient ADMM solver for computing the global minimum. An interesting finding is that, while our model is weaker than the non-convex counterparts, the minimizer of the former is generally closer to the ground truth than the reconstruction from the latter. Moreover, we demonstrate that our regularizer can outperform existing regularization techniques for deblurring and super-resolution.

Item Type: Conference Proceedings
Series.: IEEE International Conference on Image Processing ICIP
Publisher: IEEE
Additional Information: 25th IEEE International Conference on Image Processing (ICIP), Athens, GREECE, OCT 07-10, 2018
Keywords: regularization; patch; ADMM; deblurring; super-resolution
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 06 Feb 2019 06:17
Last Modified: 06 Feb 2019 06:18
URI: http://eprints.iisc.ac.in/id/eprint/61605

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