Multi-View Learning with Context-Guided Receptance for Image Denoising

• URL: http://arxiv.org/abs/2505.02705v1
• Date: Mon, 05 May 2025 14:57:43 GMT
• Title: Multi-View Learning with Context-Guided Receptance for Image Denoising
• Authors: Binghong Chen, Tingting Chai, Wei Jiang, Yuanrong Xu, Guanglu Zhou, Xiangqian Wu,
• Abstract summary: Image denoising is essential in low-level vision applications such as photography and automated driving.<n>Existing methods struggle with distinguishing complex noise patterns in real-world scenes and consume significant computational resources.<n>In this work, a Context-guided Receptance Weighted Key-Value (M) model is proposed, combining enhanced multi-view feature integration with efficient sequence modeling.<n>The model is validated on multiple real-world image denoising datasets, outperforming the existing state-of-the-art methods quantitatively and reducing inference time up to 40%.
• Score: 18.175992709188026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image denoising is essential in low-level vision applications such as photography and automated driving. Existing methods struggle with distinguishing complex noise patterns in real-world scenes and consume significant computational resources due to reliance on Transformer-based models. In this work, the Context-guided Receptance Weighted Key-Value (\M) model is proposed, combining enhanced multi-view feature integration with efficient sequence modeling. Our approach introduces the Context-guided Token Shift (CTS) paradigm, which effectively captures local spatial dependencies and enhance the model's ability to model real-world noise distributions. Additionally, the Frequency Mix (FMix) module extracting frequency-domain features is designed to isolate noise in high-frequency spectra, and is integrated with spatial representations through a multi-view learning process. To improve computational efficiency, the Bidirectional WKV (BiWKV) mechanism is adopted, enabling full pixel-sequence interaction with linear complexity while overcoming the causal selection constraints. The model is validated on multiple real-world image denoising datasets, outperforming the existing state-of-the-art methods quantitatively and reducing inference time up to 40\%. Qualitative results further demonstrate the ability of our model to restore fine details in various scenes.

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