Learning recurrent residual regressors for single image super-resolution

Abstract

Abstract Example regression-based single image super-resolution (SR) technique has been recognized as an effective way to produce a high-quality image with finer details from one low-resolution (LR) input. However, most current popular approaches usually establish the mappings from the LR feature space to the final HR one in one-pass scheme, which is insufficient to represent the complicated mapping relationship well. In this paper, we propose a novel single image SR framework by learning a group of linear residual regressors in a boosting manner so as to alleviate the gap between the underlying mappings and estimated mappings. In the training stage, we begin with the learning of a set of linear regressors by integrating the K-SVD dictionary learning algorithm and the ridge regression, and then further improve the HR estimate accuracy by learning multi-round residual regressors from the estimated errors in a cascade manner. Accordingly, in the testing stage more details can be gradually added into the input LR image by applying the learned multi-round residual regressors to SR reconstruction. The proposed SR method is fundamentally coarse-to-fine. Experimental results carried out on six publicly available datasets indicate that the proposed SR framework achieves promising performance in comparing with other state-of-the-art competitors in terms of both subjective and objective equality assessments.