Improving Image De-raining Using Reference-Guided Transformers

• URL: http://arxiv.org/abs/2408.00258v1
• Date: Thu, 1 Aug 2024 03:31:45 GMT
• Title: Improving Image De-raining Using Reference-Guided Transformers
• Authors: Zihao Ye, Jaehoon Cho, Changjae Oh,
• Abstract summary: We present a reference-guided de-raining filter, a transformer network that enhances de-raining results using a reference clean image as guidance. 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.
• Score: 9.867364371892693
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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