Sequential Deep Unrolling With Flow Priors For Robust Video Deraining
Xinwei Xue, Ying Ding, Pan Mu, Long Ma, Risheng Liu, Xin Fan
Abstract
Video deraining has attracted wide attention since the urgent demand of high-quality video in recent years. The indistinct details and nonideal deraining effects are the most common defects in existing techniques, whose cause lies in the insufficient usage of single-frame image and temporal information. To effectively settle video deraining, we establish a new deraining model with flow priors to simultaneously introduce spatial and temporal information for accurately depicting the enhancement model of the current frame. A sequential deep unrolling framework is substantially presented by solving this model based on optimization techniques. The ablation study indicates our effectiveness as far as the design of architecture. Plenty of subjective and objective evaluations fully demonstrate our superiority in detail recovery and deraining effects against other state-of-the-are video deraining approaches.