Comprehensive analysis and benchmarking of de-raining techniques through a rain model approach

Shokhina Badrieva; Silvano Bernabel; Sos Agaian

doi:10.1117/12.2618314

27 May 2022 Comprehensive analysis and benchmarking of de-raining techniques through a rain model approach

Shokhina Badrieva, Silvano Bernabel, Sos Agaian

Proceedings Volume 12100, Multimodal Image Exploitation and Learning 2022; 121000D (2022) https://doi.org/10.1117/12.2618314
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Adverse weather conditions, such as rain, impact the visual quality of images and significantly impact the performance of vision systems for drone-based video surveillance and self-driving car applications. It is essential to develop algorithms that can automatically remove these artifacts and not degrade the rest of the image. Several methods have been proposed in literature and practice to address this problem. They mainly focus on specific rain models, such as droplets, streaks, mist, or a combination of these. Real-life rain images are largely randomized with diverse rain sizes, types, densities, and directions. Furthermore, rain impacts various image parts differently and is often randomly distributed. Most existing de-raining algorithms can't remove drops, streaks, and mist from images simultaneously. This paper addresses this issue by reviewing existing algorithms and datasets through a rain model lens. We present surveys and quantitative benchmarking of state-of-the-art intelligence algorithms based on the rain types they aim to remove. While other review papers exist on single image de-raining, our work looks at and outlines the different algorithms and datasets available for each specific rain model. Finally, the paper makes the following contributions: • Select the most recent state of the art algorithms and show their performance for each rain type on our combination dataset called the Combination Rain Model Dataset • Offers insights on the issues that still exist in the developing field of image de-raining and future steps in the field

Conference Presentation

Citation Download Citation

Shokhina Badrieva, Silvano Bernabel, and Sos Agaian "Comprehensive analysis and benchmarking of de-raining techniques through a rain model approach", Proc. SPIE 12100, Multimodal Image Exploitation and Learning 2022, 121000D (27 May 2022); https://doi.org/10.1117/12.2618314

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