Multi-frame Super-resolution from Noisy Data

TL;DR

This paper introduces two adaptive regularisers, including a novel sector diffusion method, to improve multi-frame super-resolution from noisy data, demonstrating superior performance in challenging noisy scenarios.

Contribution

It presents a new non-local anisotropic diffusion regulariser called sector diffusion and evaluates its effectiveness within various super-resolution models under noisy conditions.

Findings

Sector diffusion outperforms classical regularisers in noisy super-resolution.

Evaluation reveals different model rankings in noisy versus noise-free scenarios.

Adaptive regularisers significantly improve super-resolution quality in noisy data.

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).