Combination of Single and Multi-frame Image Super-resolution: An Analytical Perspective
This work addresses a neglected theoretical gap in image super-resolution for researchers, but it is incremental as it builds on existing methods without broad application impact.
The paper tackles the problem of theoretically analyzing the optimal combination of single and multi-frame image super-resolution methods, proposing a novel analysis based on the iterative shrinkage and thresholding algorithm, with simulation results showing quantitative and qualitative support for the findings.
Super-resolution is the process of obtaining a high-resolution image from one or more low-resolution images. Single image super-resolution (SISR) and multi-frame super-resolution (MFSR) methods have been evolved almost independently for years. A neglected study in this field is the theoretical analysis of finding the optimum combination of SISR and MFSR. To fill this gap, we propose a novel theoretical analysis based on the iterative shrinkage and thresholding algorithm. We implement and compare several approaches for combining SISR and MFSR, and simulation results support the finding of our theoretical analysis, both quantitatively and qualitatively.