DRD-Net: Detail-recovery Image Deraining via Context Aggregation Networks
This addresses image quality issues in computer vision for applications like photography or surveillance, but it is incremental as it builds on existing learning-based deraining approaches.
The authors tackled the problem of image deraining, where existing methods often degrade image details, and proposed DRD-Net, which outperformed state-of-the-art methods on four datasets in terms of deraining robustness and detail accuracy.
Image deraining is a fundamental, yet not well-solved problem in computer vision and graphics. The traditional image deraining approaches commonly behave ineffectively in medium and heavy rain removal, while the learning-based ones lead to image degradations such as the loss of image details, halo artifacts and/or color distortion. Unlike existing image deraining approaches that lack the detail-recovery mechanism, we propose an end-to-end detail-recovery image deraining network (termed a DRD-Net) for single images. We for the first time introduce two sub-networks with a comprehensive loss function which synergize to derain and recover the lost details caused by deraining. We have three key contributions. First, we present a rain residual network to remove rain streaks from the rainy images, which combines the squeeze-and-excitation (SE) operation with residual blocks to make full advantage of spatial contextual information. Second, we design a new connection style block, named structure detail context aggregation block (SDCAB), which aggregates context feature information and has a large reception field. Third, benefiting from the SDCAB, we construct a detail repair network to encourage the lost details to return for eliminating image degradations. We have validated our approach on four recognized datasets (three synthetic and one real-world). Both quantitative and qualitative comparisons show that our approach outperforms the state-of-the-art deraining methods in terms of the deraining robustness and detail accuracy. The source code has been available for public evaluation and use on GitHub.