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Single Noisy Image Super Resolution by Minimizing Nuclear Norm in Virtual Sparse Domain

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 841)

Abstract

Super-resolving a noisy image is a challenging problem, and needs special care as compared to the conventional super resolution approaches, when the power of noise is unknown. In this scenario, we propose an approach to super-resolve single noisy image by minimizing nuclear norm in a virtual sparse domain that tunes with the power of noise via parameter learning. The approach minimizes nuclear norm to explore the inherent low-rank structure of visual data, and is further augmented with coarse-to-fine information by adaptively re-aligning the data along the principal components of a dictionary in virtual sparse domain. The experimental results demonstrate the robustness of our approach across different powers of noise.

Keywords

Super resolution Noise Nuclear norm Virtual sparsity Dictionary 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Image Processing and Computer Vision Lab, Department of Electrical EngineeringIIT MadrasChennaiIndia

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