{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T11:31:44Z","timestamp":1773401504502,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T00:00:00Z","timestamp":1667088000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Academy of Broadcasting Science","award":["JBKY20220210"],"award-info":[{"award-number":["JBKY20220210"]}]},{"name":"Academy of Broadcasting Science","award":["JBKY20220250"],"award-info":[{"award-number":["JBKY20220250"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recently, with the rapid development of mobile sensing technology, capturing scene information by mobile sensing devices in the form of images or videos has become a prevalent recording method. However, the moir\u00e9 pattern phenomenon may occur when the scene contains digital screens or regular strips, which greatly degrade the visual performance and image quality. In this paper, considering the complexity and diversity of moir\u00e9 patterns, we propose a novel end-to-end image demoir\u00e9 method, which can learn moir\u00e9 pattern elimination in both the frequency and spatial domains. To be specific, in the frequency domain, considering the signal energy of moir\u00e9 pattern is widely distributed in the frequency, we introduce a wavelet transform to decompose the multi-scale image features, which can help the model identify the moir\u00e9 features more precisely to suppress them effectively. On the other hand, we also design a spatial domain demoir\u00e9 block (SDDB). The SDDB module can extract moir\u00e9 features from the mixed features, then subtract them to obtain clean image features. The combination of the frequency domain and the spatial domain enhances the model\u2019s ability in terms of moir\u00e9 feature recognition and elimination. Finally, extensive experiments demonstrate the superior performance of our proposed method to other state-of-the-art methods. The Grad-CAM results in our ablation study fully indicate the effectiveness of the two proposed blocks in our method.<\/jats:p>","DOI":"10.3390\/s22218322","type":"journal-article","created":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T10:47:57Z","timestamp":1667126877000},"page":"8322","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Learning Moir\u00e9 Pattern Elimination in Both Frequency and Spatial Domains for Image Demoir\u00e9ing"],"prefix":"10.3390","volume":"22","author":[{"given":"Chenming","family":"Liu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China"},{"name":"Academy of Broadcasting Science, National Radio of Television Administration, Beijing 100866, China"},{"name":"Key Laboratory of Convergent Media and Intelligent Technology, Ministry of Education, Communication University of China, Beijing 100024, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1370-8150","authenticated-orcid":false,"given":"Yongbin","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China"},{"name":"Key Laboratory of Convergent Media and Intelligent Technology, Ministry of Education, Communication University of China, Beijing 100024, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5955-062X","authenticated-orcid":false,"given":"Nenghuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China"},{"name":"Key Laboratory of Convergent Media and Intelligent Technology, Ministry of Education, Communication University of China, Beijing 100024, China"}]},{"given":"Ruipeng","family":"Gang","sequence":"additional","affiliation":[{"name":"Academy of Broadcasting Science, National Radio of Television Administration, Beijing 100866, China"}]},{"given":"Sai","family":"Ma","sequence":"additional","affiliation":[{"name":"Academy of Broadcasting Science, National Radio of Television Administration, Beijing 100866, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yan, M., Li, S., Chan, C.A., Shen, Y., and Yu, Y. 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