Deep learning based single image deraining: datasets, metrics and methods

Xinyi Liu

doi:10.1117/12.2679282

25 May 2023 Deep learning based single image deraining: datasets, metrics and methods

Xinyi Liu

Proceedings Volume 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023); 127121A (2023) https://doi.org/10.1117/12.2679282
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 2023, Huzhou, China

Abstract

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.

Citation Download Citation

Xinyi Liu "Deep learning based single image deraining: datasets, metrics and methods", Proc. SPIE 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 127121A (25 May 2023); https://doi.org/10.1117/12.2679282

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KEYWORDS

Rain

Data modeling

Education and training

Deep learning

Visualization

Image quality

Databases

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