How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution
This work addresses the super-resolution problem for image processing by incrementally combining existing learning methods to enhance detail recovery and noise robustness.
The paper tackles the challenge of integrating internal and external learning methods for super-resolution by proposing a low-rank solution that leverages their complementary and sparse attributes, achieving superior results with improved performance on noisy images and outperforming state-of-the-art methods.
Wisely utilizing the internal and external learning methods is a new challenge in super-resolution problem. To address this issue, we analyze the attributes of two methodologies and find two observations of their recovered details: 1) they are complementary in both feature space and image plane, 2) they distribute sparsely in the spatial space. These inspire us to propose a low-rank solution which effectively integrates two learning methods and then achieves a superior result. To fit this solution, the internal learning method and the external learning method are tailored to produce multiple preliminary results. Our theoretical analysis and experiment prove that the proposed low-rank solution does not require massive inputs to guarantee the performance, and thereby simplifying the design of two learning methods for the solution. Intensive experiments show the proposed solution improves the single learning method in both qualitative and quantitative assessments. Surprisingly, it shows more superior capability on noisy images and outperforms state-of-the-art methods.