Deep Learning for Seeing Through Window with Raindrops

Abstract

When taking pictures through glass window in rainy day, the images are comprised and corrupted by the raindrops adhered to glass surfaces. It is a challenging problem to remove the effect of raindrops from an image. The key task is how to accurately and robustly identify the raindrop regions in an image. This paper develops a convolutional neural network (CNN) for removing the effect of raindrops from an image. In the proposed CNN, we introduce a double attention mechanism that concurrently guides the CNN using shape-driven attention and channel re-calibration. The shape-driven attention exploits physical shape priors of raindrops, i.e. convexness and contour closedness, to accurately locate raindrops, and the channel re-calibration improves the robustness when processing raindrops with varying appearances. The experimental results show that the proposed CNN outperforms the state-of-the-art approaches in terms of both quantitative metrics and visual quality.