Multi-frame Super-resolution from Noisy Data
This work addresses the challenge of enhancing image resolution in noisy conditions for applications like medical imaging or surveillance, representing an incremental improvement over prior methods.
The paper tackles the problem of multi-frame super-resolution from noisy, clipped data by introducing a novel non-local regularizer called sector diffusion, which outperforms existing methods and yields a different performance ranking in noisy versus noise-free scenarios.
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).