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Kernel Regularization for Image Restoration

Unni, VS and Chaudhury, KN (2020) Kernel Regularization for Image Restoration. In: Proceedings - International Conference on Image Processing, ICIP, 25-28 September 2020, Abu Dhabi; United Arab Emirates, pp. 943-947.

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Official URL: https://dx.doi.org/10.1109/ICIP40778.2020.9190938


Modern regularizers for image restoration are mostly nonquadratic and nonsmooth. They have been intensely researched and their capacity for promoting sparsity has been successfully exploited. In particular, nonquadratic regularizers are known to perform better than classical quadratic regularizers. However, in this work, we propose a quadratic regularizer of the form x top Qx whose restoration capacity is superior to total-variation and Hessian regularization. The catch is that, unlike classical regularization (e.g. Tikhonov), the matrix Q is data-driven-it is computed from the observed image via a kernel (affinity) matrix. For linear restoration problems with quadratic data-fidelity (e.g. superresolution and deconvolution), the overall optimization reduces to solving a linear system; this can be done efficiently using conjugate gradient. The attractive aspect is that we are able to avoid the inner iterations in total-variation and Hessian regularization. In a sense, the proposed regularizer combines the computational efficiency of quadratic regularizers and the restoration (image modeling) power of nonquadratic regularizers. © 2020 IEEE.

Item Type: Conference Paper
Publication: Proceedings - International Conference on Image Processing, ICIP
Publisher: IEEE Computer Society
Additional Information: cited By 0; Conference of 2020 IEEE International Conference on Image Processing, ICIP 2020 ; Conference Date: 25 September 2020 Through 28 September 2020; Conference Code:165772
Keywords: Computational efficiency; Linear systems; Matrix algebra; Restoration, Data driven; Data fidelity; Image modeling; Inner iteration; Regularizer; Restoration problems; Super resolution; Total variation, Image reconstruction
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 20 Jan 2021 06:14
Last Modified: 20 Jan 2021 06:14
URI: http://eprints.iisc.ac.in/id/eprint/67731

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