A Prototype Unit for Image De-raining using Time-Lapse Data


Jaehoon Cho (Hyundai Motor Company), Minjung Yoo (Korea Aerospace University), Jini Yang (Korea Aerospace University), Sunok Kim (Korea Aerospace University)
The 35th British Machine Vision Conference
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Abstract

We address the challenge of single-image de-raining, a task that involves recovering rain-free background information from a single rain image. While recent advancements have utilized real-world time-lapse data for training, enabling the estimation of consistent backgrounds and realistic rain streaks, these methods often suffer from computational and memory consumption, limiting their applicability in real-world scenarios. In this paper, we introduce a novel solution: the Rain Streak Prototype Unit (RsPU). The RsPU efficiently encodes rain streak-relevant features as real-time prototypes derived from time-lapse data, eliminating the need for excessive memory resources. Our de-raining network combines encoder-decoder networks with the RsPU, allowing us to learn and encapsulate diverse rain streak-relevant features as concise prototypes, employing an attention-based approach. To ensure the effectiveness of our approach, we propose a feature prototype loss encompassing cohesion and divergence components. This loss function captures both the compactness and diversity aspects of the prototypical rain streak features within the RsPU. Our method evaluates various de-raining benchmarks, accompanied by comprehensive ablation studies. We show that it can achieve competitive results in various rain images compared to state-of-the-art methods.

Citation

@inproceedings{Cho_2024_BMVC,
author    = {Jaehoon Cho and Minjung Yoo and Jini Yang and Sunok Kim},
title     = {A Prototype Unit for Image De-raining using Time-Lapse Data},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0595.pdf}
}


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