Synthetic rain image generation via CycleGAN

Abstract

In the domain of autonomous driving and computer vision, the formidable challenge of adverse weather conditions, particularly rainy weather, profoundly impacts image quality and visibility. Rain streaks pose a significant impediment to accurate object detection, especially concerning pedestrians and vehicles on the road. While existing solutions have focused on training rain removal models on paired images, acquiring such data with congruent backgrounds has been a substantial hurdle. Three distinct images, each featuring a diverse background, were deliberately chosen for the rain removal experiment. The deliberate selection of these varied backgrounds allowed us to rigorously assess the efficacy of our rain removal method across diverse environmental contexts. The compelling results obtained from this experiment affirm the method's ability to effectively mitigate rain line across a spectrum of backgrounds, thus establishing its robustness and versatility in real-world scenarios. The resulting images are then employed to train our rain removal model, marking a significant advancement in our endeavor. Our innovative approach stands poised to revolutionize rain removal techniques, aligning synthetic and authentic rain images more closely in real-world scenarios.