Multi-scale Residual Dense Block for Video Super-Resolution

Abstract

Recent studies on video super-resolution (SR) has shown that convolutional neural network (CNN) combined with motion compensation (MC) is able to merge information from multiple low-resolution (LR) frames to generate high quality images. However, Most SR and MC modules based on deep CNN simply increase the depth of the network, which cannot make full use of hierarchical and multi-scale features, thereby achieving relatively low performance. To address the above problem, a novel multi-scale residual dense Network (MSRDN) is proposed in this paper. A multi-scale residual density block (MSRDB) is first designed to extract abundant local features through dense convolution layer, which helps to adaptively detect image features of different scales with convolution kernels of different scales. Then, we redesign SR module and MC module with MSRDB, which adaptively learns more effective features from local features and uses global feature fusion to jointly and adaptively learn global hierarchical features. Comparative results on Vid4 dataset demonstrate that MSRDB can make more full use of feature information, which helps to effectively improve the reconstruction performance of video SR.