Sun, J.; Yuan, Q.; Shen, H.; Li, J.; Zhang, L. A Single-Frame and Multi-Frame Cascaded Image Super-Resolution Method. Preprints2024, 2024080253. https://doi.org/10.20944/preprints202408.0253.v1
APA Style
Sun, J., Yuan, Q., Shen, H., Li, J., & Zhang, L. (2024). A Single-Frame and Multi-Frame Cascaded Image Super-Resolution Method. Preprints. https://doi.org/10.20944/preprints202408.0253.v1
Chicago/Turabian Style
Sun, J., Jie Li and Liangpei Zhang. 2024 "A Single-Frame and Multi-Frame Cascaded Image Super-Resolution Method" Preprints. https://doi.org/10.20944/preprints202408.0253.v1
Abstract
The objective of image super-resolution is to reconstruct a high-resolution (HR) image with the prior knowledge from one or several low-resolution (LR) images. However, in the real world, due to the limited complementary information, the performance of both single-frame and multi-frame super-resolution reconstruction degrades rapidly as the magnification increases. In this paper, we propose a novel two-step image super resolution method concatenating multi-frame super-resolution (MFSR) with single-frame super-resolution (SFSR), to progressively upsample images to the desired resolution. The proposed method consisting of an L0-norm constrained reconstruction scheme and an enhanced residual back-projection network, integrating the flexibility of the variational model-based method and the feature learning capacity of the deep learning-based method. To verify the effectiveness of the proposed algorithm, extensive experiments with both simulated and real world sequences were implemented. The experimental results show that the proposed method yields superior performance in both objective and perceptual quality measurements, compared to the baseline super-resolution algorithms in the cascade model. In addition, the experiment indicates that this cascade model can be robustly applied to different SFSR and MFSR methods.
Keywords
Super-resolution; deep learning; cascade model; resolution enhancement; regularized framework
Subject
Computer Science and Mathematics, Signal Processing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
