Video super-resolution using joint regularization

摘要

Video super-resolution (SR) is an inverse problem, and with this method, we can reconstruct a high-resolution (HR) version of a low-resolution (LR) video sequence. Because regularization-based method can solve the pathological problem in super-resolution, so it is widely used. However, in many traditional regularization terms, only the intra-image correlation will be taken into consideration so that the redundancy between adjacent frames is not be utilized. In order to make full use of both inter-image correlation and intra-image correlation, we combine compensation-based TV (CTV) regularization term with multi-non-local low-rank (MNLR) regularization term in our algorithm. Moreover, we utilize a weight matrix to reduce the negative impacts which is caused by registration residuals in CTV, and the weight matrix is based on spatial information filtering and clustering. The experiments show that we can get better results than the compared methods by the proposed algorithm in visual quality and objective effective evaluation.