New single-image super-resolution reconstruction using MRF model

Abstract

Abstract Learning-based single-image super-resolution reconstruction (LSISRR) problem using Markov Random Field (LSISRR-MRF) is a robust and integrated framework for both natural and face image super-resolution. The spatial relationships between patches are learned from the training images using the Markov network, and the missing details in the unknown high-resolution (HR) images are predicted using the learned relationship. However, selection of efficient training images with better structural similarity to the input low-resolution (LR) image and appropriate selection of compatibility constraints to measure the correlation between patches affect a lot to the reconstruction results. Besides these, selection of optimal candidate patches for each input LR patch from a pool of training patches is a time-consuming process. To avoid these pitfalls, an improved and fast LSISRR-MRF model is proposed here. A statistical framework, i.e., spatiogram based matching is used to choose efficient training images. Searching of candidate patches is performed in a compact and well-structured search space known as epitomic representation. Smoothness preserving compatibility functions are utilized to well constraint the correlation between patches. Efficacy of the suggested method for generating a high-quality HR image is validated via several experimental analyses on both synthetic and real-time images over some of the state of art methods.