Deep Layer Prior Optimization for Single Image Rain Streaks Removal

Visible distortions caused by rain streaks have significant negative effects on the performance of many vision and learning algorithms. Most of the existing deraining approaches propose to build complex prior models to formulate the appearance of rain streaks. Unfortunately, these human-designed priors tend to over-smooth the background and leave too many rain streaks since the distribution of rain streaks is complex and disordered. In this work, we exploit a deep layer prior under the maximum a posterior framework to recover the intrinsic rain structure. The optimization of the resulted variational energy can be understood as simultaneously performing rain and image propagations based on data-dependent residual networks and task cues (e.g., total variation regularization), respectively. Experimental results on both synthetic and real test images demonstrate the effectiveness of our approach against both designed priors and fully data-dependent convolutional neural networks.