Joint rain and atmospheric veil removal from single image

In natural rainy scenes, visibility is significantly degraded by two types of phenomena: specular highlights of nearby individual rain streaks and atmospheric veiling effect caused by distant accumulated rain. However, most existing deraining methods only take the first kind of degradation into consideration, which limits their potential application in heavy rain. In this study, a joint rain and atmospheric veil removal framework is proposed to address this problem. Since rain streaks and rain accumulation are entangled with each other, which is intractable to simulate, causing clean/rainy image pairs of real-world are hard to generate. Hence, after introducing a generalised rain model, which can represent both rain streaks and atmospheric veil physically, the authors do not learn the mapping function between image pairs using deep-learning architecture, but estimate the rain streaks, transmission, and atmospheric light via Gaussian mixture model patch prior and dark channel prior to solve the rain model instead. According to the comprehensive experimental evaluations, the proposed method outperforms other state-of-the-art methods in terms of both high visibility and vivid colour, especially in natural heavy rain scenario.