Image super-resolution using matrix valued operations
Matrix valued image super-resolution is a novel topic of research that uses the spatial dependencies when finding the mapping between low and high resolution images. This project implements this paper, and provides a proof-of-concept.
The data should be downloaded from this link, and moved to the Data folder.
We train the model using trainingPatchExtraction.m and the test scripts are provided in testingPatchExtraction.m.
The scripts psnrCalc.m and ssim_wl.m are provided for model evaluation.
The paper is provided at Single Image SISR