Formula omitted] structure-prior assisted blur-intensity aware efficient video deblurring

Abstract

In this paper, we propose a L0 Structure-Prior Assisted Blur-intensity Aware method for efficient video deblurring. To efficiently generate sharp frames, we utilize separate streams to estimate content and structure information, both two streams effectively extract features under dedicated constraints using light-weight architecture. In content estimation, to better extract features from frames with non-uniform blur distribution, we introduce a blur-intensity detection module to generate a blur-intensity mask. The mask reflects region-wise blur degrees similar to an attention map and provides preliminary information to the content decoder. For structure estimation, to generate frames with less structural distortion, we utilize L0 smoothed frames as supervision to estimate structure information. A pyramid module with a deformable convolution layer in the last scale is designed in this stream. The estimated content and structure information are then merged with the frame synthesis module to generate perceptually favorable frames. Experiments demonstrate that our method could achieve favorable results on both synthetic and real-world datasets in terms of objective metrics and perceptual quality, with fewer input frames and less time-consuming.