Rain removal using cycle-consistency adversarial network - Ng, Henry Siong Hock (2019)
Raindrops in videos and images can hamper the visibility of objects in a scene, leading to a loss of video quality. In this project, we address the problem of rain removal in images by using an unsupervised learning approach relying on a new framework of cycle-consistent generative adversarial networks. Unlike usual image domain transfer problem, the proposed solution solves the problem by having two asymmetric functions: a forward function that removes the rain from a rain degraded image and a backward function that adds rain into a rain-free clean image. The main idea is to have two coupled generative adversarial network that implements these two functions: one that would remove rain from a rain degraded image and a second network that would add rain into a rain-free clean image. Our experiments show the effectiveness of our approach and how it performs against other previous works.