Single-image super resolution via hashing classification and sparse representation
Liang Peng, Junmei Yang · 2017
Single image super-resolution technology is a fundamental and important issue in the field of computer vision. This paper presents a hash-based classified dictionary learning method to reconstruct images. Firstly, calculate hash value of each image patches and classify the image patches according to its hash values. Then tightly sub-dictionary is learned for each cluster. For a given test image patch, corresponding sub-dictionary is selected by hash value, and then super-resolution reconstruction of this image is complete. The experimental results show that the proposed method is superior to the recently proposed dictionary learning methods for image super-resolution resolution in details and ensures quality of the reconstructed images.