Multi-frame Super-resolution from Noisy Data

• URL: http://arxiv.org/abs/2103.13778v1
• Date: Thu, 25 Mar 2021 12:07:08 GMT
• Title: Multi-frame Super-resolution from Noisy Data
• Authors: Kireeti Bodduna and Joachim Weickert
• Abstract summary: We show the usefulness of two adaptive regularisers based on anisotropic diffusion ideas. We also introduce a novel non-local one with one-sided differences and superior performance. Surprisingly, the evaluation in a practically relevant noisy scenario produces a different ranking than the one in the noise-free setting.
• Score: 6.414055487487486
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Obtaining high resolution images from low resolution data with clipped noise is algorithmically challenging due to the ill-posed nature of the problem. So far such problems have hardly been tackled, and the few existing approaches use simplistic regularisers. We show the usefulness of two adaptive regularisers based on anisotropic diffusion ideas: Apart from evaluating the classical edge-enhancing anisotropic diffusion regulariser, we introduce a novel non-local one with one-sided differences and superior performance. It is termed sector diffusion. We combine it with all six variants of the classical super-resolution observational model that arise from permutations of its three operators for warping, blurring, and downsampling. Surprisingly, the evaluation in a practically relevant noisy scenario produces a different ranking than the one in the noise-free setting in our previous work (SSVM 2017).

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