Super resolution image reconstruction via dual dictionary learning in sparse environment

<span lang="EN-US">Patch-based super resolution is a method in which spatial features from low-resolution (LR) patch are used as references for the reconstruction of high-resolution (HR) image patches. Sparse representation each extracted. These coefficients obtained to recover HR patch. One dictionary trained LR patches, and another patches both dictionaries jointly trained. In proposed method, high frequency (HF) details required treated combination main (MHF) residual (RHF). Hence, dual-dictionary learning learning. This MHF RHF respectively recovering finer details. Experiments carried out test technique on different images. The results illustrate efficacy algorithm.</span>