Confidence Measure Guided Single Image De-raining
This work addresses the challenge of removing varying rain streaks from images, which is important for applications like autonomous driving and surveillance, but it appears incremental as it builds on prior de-raining approaches.
The paper tackles the problem of single image de-raining by addressing the limitation of previous methods that ignore location-specific rain information, resulting in significant improvements over state-of-the-art methods on synthetic and real datasets.
Single image de-raining is an extremely challenging problem since the rainy images contain rain streaks which often vary in size, direction and density. This varying characteristic of rain streaks affect different parts of the image differently. Previous approaches have attempted to address this problem by leveraging some prior information to remove rain streaks from a single image. One of the major limitations of these approaches is that they do not consider the location information of rain drops in the image. The proposed Image Quality-based single image Deraining using Confidence measure (QuDeC), network addresses this issue by learning the quality or distortion level of each patch in the rainy image, and further processes this information to learn the rain content at different scales. In addition, we introduce a technique which guides the network to learn the network weights based on the confidence measure about the estimate of both quality at each location and residual rain streak information (residual map). Extensive experiments on synthetic and real datasets demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art methods.