Wide Separate 3D Convolution for Video Super Resolution Video superresolution (VSR) aims to recover realistic highresolution (HR) frame from its corresponding center lowresolution (LR) frame and some neighbouring supporting frames. To utilize the extra temporal information of supporting LR frames, most of VSR methods highly rely on accurate motion estimation and compensation models to align LR frames. However, the motions between frames have no ground truth, and it is difficult to train motion estimation and compensation models. Inaccurate results will lead to artifacts and blurs, which also will damage the recovery of highresolution frames. We propose an effective separate 3D Convolution Neural Network (CNN) with wide activation to overcome the drawback of utilizing motion estimation and compensation models. Separate 3D convolution is factorizing the 3D convolution into 2D convolution along spatial domain and 1D convolution along temporal domain, which can not only capture temporal and spatial information simultaneously but also reduce the computation complexity compared to 3D CNN.

Video SuperResolution with Compensation in Feature Extraction Conventional Convolutional Neural Network (CNN) based video superresolution (VSR) methods heavily depend on motion compensation. Pixels in input frames are warped according to flowlike information to eliminate interframe differences. These methods have to make a tradeoff between the distraction caused by spatiotemporal inconsistency and the pixelwise detail damage caused by compensation. In this paper, we propose a novel video superresolution method with a dynamic filter network based compensation module and a residual network based SR module. Unlike traditional VSR techniques, our method does not warp input pixels, but performs motion compensation during feature extractions. The experimental results demonstrate that our method outperforms the stateoftheart VSR algorithms by at least 1.08 dB in terms of PSNR, and recovers more details together with superior visual quality.