Deep learning based single image deraining: datasets, metrics and methods
Xinyi Liu · 2023
Removing the rain stripe from the image obtained in a wet setting improves visual identification accuracy. This study focuses on both classic and deep learning models to track the research progress and trends. Various metrics including SSIM and PSNR have been designed for evaluating these models, based on some widely used open image datasets, e.g., Rain12 and Rain100L. Both synthetic datasets, which are based on additive composite model and rainstorm model, and real-world datasets are used in the literature. The improvements of several models over baselines are summarized. This paper also covers the major unresolved topics in current research. When applied to the actual world, several powerful algorithms still struggle to suppress edge artifacts, weigh definitions, and understand structural subtleties. Open issues including rain water direction, quantity, and future directions are also highlighted to encourage further research.