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arXiv · 2408.00258

Improving Image De-raining Using Reference-Guided Transformers

Zihao Ye·Jaehoon Cho·Changjae Oh
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Abstract

Image de-raining is a critical task in computer vision to improve visibility and enhance the robustness of outdoor vision systems. While recent advances in de-raining methods have achieved remarkable performance, the challenge remains to produce high-quality and visually pleasing de-rained results. In this paper, we present a reference-guided de-raining filter, a transformer network that enhances de-raining results using a reference clean image as guidance. We leverage the capabilities of the proposed module to further refine the images de-rained by existing methods. We validate our method on three datasets and show that our module can improve the performance of existing prior-based, CNN-based, and transformer-based approaches.

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Zihao Ye, Jaehoon Cho, Changjae Oh. 2024-08-01. Improving Image De-raining Using Reference-Guided Transformers. https://arxiv.org/abs/2408.00258

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