MobileDeRainGAN: An Efficient Semi-Supervised Approach to Single Image Rain Removal for Task-Driven Applications

Abstract

Rain removal is an essential task in computer vision, particularly for applications such as autonomous navigation to function seamlessly during rain. However, most existing single-image deraining algorithms are limited by their inability to generalize on diverse real-world rainy images, the need for real-time processing, and the lack of task-driven metric enhancement. This paper proposes MobileDeRainGAN, an efficient semi-supervised algorithm that addresses these challenges. The proposed approach includes a novel latent bridge network and multi-scale discriminator that effectively removes rain-related artifacts at different scales. Our cross-domain experiments on Rain1400 and RainCityscapes datasets demonstrate substantial improvements over state-of-the-art methods in terms of generalization and object detection scores in a semi-supervised setting. Furthermore, our approach is significantly faster and can run in real-time even on edge devices. Overall, our proposed MobileDeRainGAN algorithm offers a significant improvement in rain removal performance on real-world images while being efficient, scalable, and suitable for real-world applications.