LAUSR

Restoring a rainy image with raindrops or rainstreaks of varying scales, directions, and densities is an extremely challenging task. Recent approaches attempt to leverage the rain distribution (e.g., location) as prior to generate satisfactory results. However, concatenation of a single distribution map with the rainy image or with intermediate feature maps is too simplistic to fully exploit the advantages of such priors. To further explore this valuable information, an advanced cascaded attention guidance network, dubbed as CAG-Net, is formulated and designed as a three-stage model. In the first stage, a multi-task learning network is constructed for producing the attention map and coarse de-raining results simultaneously. Subsequently, the coarse results and the rain distribution map are concatenated and fed to the second stage for results refinement. In this stage, the attention map generation network from the first stage is used to formulate a novel semantic consistency loss for better detail recovery. In the third stage, a novel pyramidal “where-and-how” learning mechanism is formulated. At each pyramid level, a two-branch network is designed to take the features from previous stages as inputs to generate better attention-guidance features and de-raining features, which are then combined via a gating scheme to produce the final de-raining results. Moreover, the uncertainty maps are also generated in this stage for more accurate pixel-wise loss calculation. Extensive experiments are carried out for removing raindrops or rainstreaks from both synthetic and real rainy images, and CAG-Net is demonstrated to produce significantly better results than state-of-the-art models. Codes are available at <https://github.com/RobinCSIRO/CAGNet>.