Bidirectional Multi-Scale Implicit Neural Representations for Image Deraining

• URL: http://arxiv.org/abs/2404.01547v1
• Date: Tue, 2 Apr 2024 01:18:16 GMT
• Title: Bidirectional Multi-Scale Implicit Neural Representations for Image Deraining
• Authors: Xiang Chen, Jinshan Pan, Jiangxin Dong,
• Abstract summary: We develop an end-to-end multi-scale Transformer to facilitate high-quality image reconstruction. We incorporate intra-scale implicit neural representations based on pixel coordinates with the degraded inputs in a closed-loop design. Our approach, named as NeRD-Rain, performs favorably against the state-of-the-art ones on both synthetic and real-world benchmark datasets.
• Score: 47.15857899099733
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: How to effectively explore multi-scale representations of rain streaks is important for image deraining. In contrast to existing Transformer-based methods that depend mostly on single-scale rain appearance, we develop an end-to-end multi-scale Transformer that leverages the potentially useful features in various scales to facilitate high-quality image reconstruction. To better explore the common degradation representations from spatially-varying rain streaks, we incorporate intra-scale implicit neural representations based on pixel coordinates with the degraded inputs in a closed-loop design, enabling the learned features to facilitate rain removal and improve the robustness of the model in complex scenarios. To ensure richer collaborative representation from different scales, we embed a simple yet effective inter-scale bidirectional feedback operation into our multi-scale Transformer by performing coarse-to-fine and fine-to-coarse information communication. Extensive experiments demonstrate that our approach, named as NeRD-Rain, performs favorably against the state-of-the-art ones on both synthetic and real-world benchmark datasets. The source code and trained models are available at https://github.com/cschenxiang/NeRD-Rain.

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