{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T07:03:29Z","timestamp":1763795009626,"version":"3.45.0"},"reference-count":49,"publisher":"Association for Computing Machinery (ACM)","issue":"12","funder":[{"DOI":"10.13039\/501100001352","name":"National University of Singapore","doi-asserted-by":"crossref","award":["ELDT-RP2"],"award-info":[{"award-number":["ELDT-RP2"]}],"id":[{"id":"10.13039\/501100001352","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2025,12,31]]},"abstract":"<jats:p>\n                    Photographing optoelectronic displays often introduces unwanted moir\u00e9 patterns due to analog signal interference between the pixel grids of the display and the camera sensor arrays. This work identifies two problems that are largely ignored by existing image demoir\u00e9ing approaches: (1) moir\u00e9 patterns vary across different channels (RGB); (2) repetitive patterns are constantly observed. However, employing conventional convolutional (CNN) layers cannot address these problems. Instead, this article presents the use of our recently proposed\n                    <jats:italic toggle=\"yes\">Shape<\/jats:italic>\n                    concept. It was originally employed to model consistent features from fragmented regions, particularly when identical or similar objects coexist in an RGB-D image. Interestingly, we find that the\n                    <jats:italic toggle=\"yes\">Shape<\/jats:italic>\n                    information effectively captures the moir\u00e9 patterns in artifact images. Motivated by this discovery, we propose a new method, ShapeMoir\u00e9, for image demoir\u00e9ing. Beyond modeling shape features at the patch level, we further extend this to the global image level and design a novel Shape-Architecture. Consequently, our proposed method, equipped with both ShapeConv and Shape-Architecture, can be seamlessly integrated into existing approaches without introducing any additional parameters or computation overhead during inference. We conduct extensive experiments on four widely used datasets, and the results demonstrate that our ShapeMoir\u00e9 achieves state-of-the-art performance, particularly in terms of the PSNR metric. We then apply our method across four popular architectures to showcase its generalization capabilities. Moreover, to further validate its generality beyond the demoir\u00e9ing task, we apply ShapeMoir\u00e9 to the image deblurring task, where it continues to deliver consistent performance gains. Finally, experiments on real-world images captured by smartphones confirm the robustness and practical applicability of ShapeMoir\u00e9 in challenging demoir\u00e9ing scenarios. We open sourced an implementation of ShapeMoir\u00e9 in PyTorch at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/SichengS\/ShapeMoire\">https:\/\/github.com\/SichengS\/ShapeMoire<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3748657","type":"journal-article","created":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:50:57Z","timestamp":1760104257000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["ShapeMoir\u00e9: Channel-Wise Shape-Guided Network for Image Demoir\u00e9ing"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8614-7366","authenticated-orcid":false,"given":"Jinming","family":"Cao","sequence":"first","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3154-5089","authenticated-orcid":false,"given":"Sicheng","family":"Shen","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5647-2253","authenticated-orcid":false,"given":"Qiu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6525-6133","authenticated-orcid":false,"given":"Yifang","family":"Yin","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research (I2R), A*STAR, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4039-4705","authenticated-orcid":false,"given":"Yangyan","family":"Li","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7410-2590","authenticated-orcid":false,"given":"Roger","family":"Zimmermann","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,11,21]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58607-2_7"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00700"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/2726947"},{"key":"e_1_3_2_5_2","first-page":"3486","volume-title":"Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW)","author":"Cheng Xi","year":"2019","unstructured":"Xi Cheng, Zhenyong Fu, and Jian Yang. 2019. 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