How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution

TL;DR

This paper introduces a low-rank matrix decomposition approach to effectively combine internal and external learning methods for super-resolution, leading to improved results especially on noisy images.

Contribution

The paper proposes a novel low-rank solution that integrates internal and external learning methods for super-resolution, reducing input requirements and enhancing performance.

Findings

Improves super-resolution quality over single learning methods.

Performs well on noisy images, surpassing state-of-the-art methods.

Requires fewer inputs for effective performance.

Abstract

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.