Single Image Deraining via Rain-Steaks Aware Deep Convolutional Neural Network
This work addresses the challenge of single image deraining, which is useful for applications like autonomous navigation in rainy conditions, but it appears incremental as it builds on existing techniques.
The paper tackles the problem of removing spatially varying rain streaks from a single rainy image by proposing a method that combines an improved weighted guided image filter with a rain-streaks aware deep convolutional neural network, resulting in significant outperformance over state-of-the-art methods on synthetic and real-world images.
It is challenging to remove rain-steaks from a single rainy image because the rain steaks are spatially varying in the rainy image. This problem is studied in this paper by combining conventional image processing techniques and deep learning based techniques. An improved weighted guided image filter (iWGIF) is proposed to extract high frequency information from a rainy image. The high frequency information mainly includes rain steaks and noise, and it can guide the rain steaks aware deep convolutional neural network (RSADCNN) to pay more attention to rain steaks. The efficiency and explain-ability of RSADNN are improved. Experiments show that the proposed algorithm significantly outperforms state-of-the-art methods on both synthetic and real-world images in terms of both qualitative and quantitative measures. It is useful for autonomous navigation in raining conditions.