Deep neural de-raining model based on dynamic fusion of multiple vision tasks

Image quality is relevant to the performance of computer vision applications. The interference of rain streaks often greatly depreciates the visual effect of images. It is a traditional and critical vision challenge to remove rain streaks from rainy images. In this paper, we introduce a deep connectionist screen blend model for single-image rain removal research. The novel deep structure is mainly composed of shortcut connections, and ends with sibling branches. The specific architecture is designed for joint optimization of heterogeneous but related tasks. In particular, a feature-level task is design to preserve object edges which tend to be lost in de-rained images. Moreover, a comprehensive image quality assessment is an additional vision task for further improvement on de-rained results. Instead of using rules of thumb, we propose an actionable method to dynamically assign appropriate weighting coefficients for all vision tasks we use. On the other hand, various factors such as haze also give rise to weak visual appeal of rainy images. To remove these adverse factors, we develop an image enhancement framework which enables the hyperparameters to be optimized in an adaptive way, and efficiently improves the perceived quality of de-rained results. The effectiveness of the proposed de-raining system has been verified by extensive experiments, and most results of our method are impressive. The source code and more de-rained results will be available online.