Deep joint rain and haze removal from single images
This addresses image restoration for computer vision applications, but it is incremental as it builds on existing rain and haze removal techniques.
The paper tackles the problem of removing both rain and haze from single images by proposing a convolutional neural network that uses wavelet transforms and dark channel features, achieving superior performance over state-of-the-art methods in experiments on synthetic and real-world datasets.
Rain removal from a single image is a challenge which has been studied for a long time. In this paper, a novel convolutional neural network based on wavelet and dark channel is proposed. On one hand, we think that rain streaks correspond to high frequency component of the image. Therefore, haar wavelet transform is a good choice to separate the rain streaks and background to some extent. More specifically, the LL subband of a rain image is more inclined to express the background information, while LH, HL, HH subband tend to represent the rain streaks and the edges. On the other hand, the accumulation of rain streaks from long distance makes the rain image look like haze veil. We extract dark channel of rain image as a feature map in network. By increasing this mapping between the dark channel of input and output images, we achieve haze removal in an indirect way. All of the parameters are optimized by back-propagation. Experiments on both synthetic and real- world datasets reveal that our method outperforms other state-of- the-art methods from a qualitative and quantitative perspective.