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Most deraining works focus on rain streaks removal but they cannot dealadequately with heavy rain images. In heavy rain, streaks are strongly visible,dense rain accumulation or rain veiling effect significantly washes out theimage, further scenes are relatively more blurry, etc. In this paper, wepropose a novel method to address these problems. We put forth a 2-stagenetwork: a physics-based backbone followed by a depth-guided GAN refinement.The first stage estimates the rain streaks, the transmission, and theatmospheric light governed by the underlying physics. To tease out thesecomponents more reliably, a guided filtering framework is used to decompose theimage into its low- and high-frequency components. This filtering is guided bya rain-free residue image --- its content is used to set the passbands for thetwo channels in a spatially-variant manner so that the background details donot get mixed up with the rain-streaks. For the second stage, the refinementstage, we put forth a depth-guided GAN to recover the background details failedto be retrieved by the first stage, as well as correcting artefacts introducedby that stage. We have evaluated our method against the state of the artmethods. Extensive experiments show that our method outperforms them on realrain image data, recovering visually clean images with good details.

Heavy Rain Image Restoration: Integrating Physics Model and Conditional Adversarial Learning