{"type":"ScholarlyArticle","title":"Multi-View Learning with Context-Guided Receptance for Image Denoising","authors":[{"name":"Binghong Chen","slug":"binghong-chen","given":"Binghong","family":"Chen","affiliation":""},{"name":"Tingting Chai","slug":"tingting-chai","given":"Tingting","family":"Chai","affiliation":""},{"name":"Wei Jiang","slug":"wei-jiang","given":"Wei","family":"Jiang","affiliation":""},{"name":"Yuanrong Xu","slug":"yuanrong-xu","given":"Yuanrong","family":"Xu","affiliation":""},{"name":"Guanglu Zhou","slug":"guanglu-zhou","given":"Guanglu","family":"Zhou","affiliation":""},{"name":"Xiangqian Wu","slug":"xiangqian-wu","given":"Xiangqian","family":"Wu","affiliation":""}],"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 (CRWKV) model is proposed, combining enhanced multi-view feature integration with efficient sequence modeling. The Context-guided Token Shift (CTS) mechanism is introduced to effectively capture local spatial dependencies and enhance the model's ability to model real-world noise distributions. Also, 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 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. The code is publicly available at https://github.com/Seeker98/CRWKV.","doi":"10.24963/ijcai.2025/86","url":"https://ijcai.org/proceedings/2025/86","pdf":"https://ijcai.org/proceedings/2025/0086.pdf","markdown":"https://ijcai.org/proceedings/2025/86.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/86","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Computer Vision","track":"Main Track","number":86,"pages":{"first":765,"last":773},"keywords":["Computer Vision: CV: Structural and model-based approaches, knowledge representation and reasoning","Computer Vision: CV: Machine learning for vision","Computer Vision: CV: Low-level Vision","Computer Vision: CV: Representation learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}