{"ID":2893951,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.12188","arxiv_id":"2507.12188","title":"Wavelet-based Decoupling Framework for low-light Stereo Image Enhancement","abstract":"Low-light images suffer from complex degradation, and existing enhancement methods often encode all degradation factors within a single latent space. This leads to highly entangled features and strong black-box characteristics, making the model prone to shortcut learning. To mitigate the above issues, this paper proposes a wavelet-based low-light stereo image enhancement method with feature space decoupling. Our insight comes from the following findings: (1) Wavelet transform enables the independent processing of low-frequency and high-frequency information. (2) Illumination adjustment can be achieved by adjusting the low-frequency component of a low-light image, extracted through multi-level wavelet decomposition. Thus, by using wavelet transform the feature space is decomposed into a low-frequency branch for illumination adjustment and multiple high-frequency branches for texture enhancement. Additionally, stereo low-light image enhancement can extract useful cues from another view to improve enhancement. To this end, we propose a novel high-frequency guided cross-view interaction module (HF-CIM) that operates within high-frequency branches rather than across the entire feature space, effectively extracting valuable image details from the other view. Furthermore, to enhance the high-frequency information, a detail and texture enhancement module (DTEM) is proposed based on cross-attention mechanism. The model is trained on a dataset consisting of images with uniform illumination and images with non-uniform illumination. Experimental results on both real and synthetic images indicate that our algorithm offers significant advantages in light adjustment while effectively recovering high-frequency information. The code and dataset are publicly available at: https://github.com/Cherisherr/WDCI-Net.git.","short_abstract":"Low-light images suffer from complex degradation, and existing enhancement methods often encode all degradation factors within a single latent space. This leads to highly entangled features and strong black-box characteristics, making the model prone to shortcut learning. To mitigate the above issues, this paper propos...","url_abs":"https://arxiv.org/abs/2507.12188","url_pdf":"https://arxiv.org/pdf/2507.12188v1","authors":"[\"Shuangli Du\",\"Siming Yan\",\"Zhenghao Shi\",\"Zhenzhen You\",\"Lu Sun\"]","published":"2025-07-16T12:42:27Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"eess.IV\"]","methods":"[]","has_code":false,"code_links":[{"ID":612083,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2893951,"paper_url":"https://arxiv.org/abs/2507.12188","paper_title":"Wavelet-based Decoupling Framework for low-light Stereo Image Enhancement","repo_url":"https://github.com/Cherisherr/WDCI-Net.git","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}