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It plays an important role in many fields, such as remote sensing and medical imaging. How to effectively capture critical information parsimoniously for high-quality reconstruction has long been a pivotal problem in this domain. This study aims to develop an efficient and effective focal modulation scheme for image restoration. Inspired by the fact that different regions in a corrupted image always undergo degradations in various degrees, we introduce a dual-domain selection mechanism to emphasize crucial information for restoration, such as edge signals and hard regions. Moreover, a channel modulation module is developed to facilitate channel interactions by exploring the utility of the Fourier transform in channel dimensions. In addition, we split high-resolution features to insert multi-scale receptive fields into the network, improving efficiency and performance. Incorporating these designs into a U-shaped convolutional backbone, the network achieves state-of-the-art performance on 13 different datasets for five general image restoration tasks, including dehazing, desnowing, deraining, motion\/defocus deblurring, and low-light enhancement. To further demonstrate the effectiveness of our focal modulation strategy, we apply it to the all-in-one image restoration setting, and the obtained model performs favorably against state-of-the-art all-in-one algorithms. Moreover, our module extends effectively to tasks such as composite degradation, medical imaging, and ultra-high-definition image restoration.\n                  <\/jats:p>","DOI":"10.1007\/s11263-025-02589-y","type":"journal-article","created":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T09:05:13Z","timestamp":1766480713000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Focal Modulation for Image Restoration"],"prefix":"10.1007","volume":"134","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1279-5539","authenticated-orcid":false,"given":"Yuning","family":"Cui","sequence":"first","affiliation":[]},{"given":"Wenqi","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Alois","family":"Knoll","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,23]]},"reference":[{"key":"2589_CR1","unstructured":"Ixi dataset (2023). http:\/\/braindevelopment.org\/ixi-dataset\/. 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