A Deep Tree-Structured Fusion Model for Single Image Deraining
This work addresses image quality degradation due to rain for computer vision applications, representing an incremental improvement in deraining methods.
The authors tackled single image deraining by proposing a deep tree-structured fusion model that aggregates features to reduce redundancy, achieving better results with fewer parameters on synthetic and real-world datasets.
We propose a simple yet effective deep tree-structured fusion model based on feature aggregation for the deraining problem. We argue that by effectively aggregating features, a relatively simple network can still handle tough image deraining problems well. First, to capture the spatial structure of rain we use dilated convolutions as our basic network block. We then design a tree-structured fusion architecture which is deployed within each block (spatial information) and across all blocks (content information). Our method is based on the assumption that adjacent features contain redundant information. This redundancy obstructs generation of new representations and can be reduced by hierarchically fusing adjacent features. Thus, the proposed model is more compact and can effectively use spatial and content information. Experiments on synthetic and real-world datasets show that our network achieves better deraining results with fewer parameters.