Piece-Wise Kernel Regression for Example-Based Super-Resolution
- Resource Type
- Conference
- Authors
- Tang, Yi; Chen, Jun-Hua; Jiang, Zuo
- Source
- 2018 International Conference on Machine Learning and Cybernetics (ICMLC) Machine Learning and Cybernetics (ICMLC), 2018 International Conference on. 1:143-148 Jul, 2018
- Subject
- Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Image resolution
Training
Kernel
Estimation
Optimization
Testing
Manifolds
Super-resolution
Kernel regression function
Piece-wise learning
- Language
- ISSN
- 2160-1348
Many existed example-based super-resolution algorithms focus on learning a regression function which maps a low-resolution image patch to a high-resolution image patch. Even though this strategy often works well, the speciality of image patches is omitted during learning process. To this end, a novel piece-wised kernel regression algorithm for super-resolution is proposed for benefitting from the speciality of training image patches. The kernel-based regression function learned by this algorithm will vary along with the changing of low-resolution image patches, which enable the learned regression function more flexible to match the speciality of low-resolution image patches. Experimental results show the convinced performance of the reported algorithm.