Single-Image Super-Resolution via Multiple Matrix-Valued Kernel Regression

Single-image super-resolution focuses on learning a mapping to recover high-resolution images from given low-resolution images with the help of a set of paired images. Matrix-valued operators serve as an efficient mapping to super-resolve low-resolution images. However, most existed matrix-valued based super-resolution algorithms limit matrix-valued operators as linear mappings. Multiple matrix-valued operators based algorithm is introduced for improving the performance of matrix-value operators in single-image super-resolution. Taking advantages of the non-linear style of multiple matrix-valued operators, we have more accurate super-resolved images. The experimental results show the efficiency and effectiveness of the reported multiple matrix-valued operator learning based super-resolution algorithm.