{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:08:20Z","timestamp":1773245300833,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,22]],"date-time":"2024-06-22T00:00:00Z","timestamp":1719014400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development (R&amp;D) Program of China","award":["2022YFB3903401"],"award-info":[{"award-number":["2022YFB3903401"]}]},{"name":"National Key Research and Development (R&amp;D) Program of China","award":["2042022DX0001"],"award-info":[{"award-number":["2042022DX0001"]}]},{"name":"National Key Research and Development (R&amp;D) Program of China","award":["220100036"],"award-info":[{"award-number":["220100036"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2022YFB3903401"],"award-info":[{"award-number":["2022YFB3903401"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2042022DX0001"],"award-info":[{"award-number":["2042022DX0001"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["220100036"],"award-info":[{"award-number":["220100036"]}]},{"name":"Hubei Luojia Laboratory Special Fund","award":["2022YFB3903401"],"award-info":[{"award-number":["2022YFB3903401"]}]},{"name":"Hubei Luojia Laboratory Special Fund","award":["2042022DX0001"],"award-info":[{"award-number":["2042022DX0001"]}]},{"name":"Hubei Luojia Laboratory Special Fund","award":["220100036"],"award-info":[{"award-number":["220100036"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>During satellite remote sensing imaging, the use of Bayer mode sensors holds significant importance in saving airborne computing resources and reducing the burden of satellite transmission systems. The demosaicing techniques play a key role in this process. The integration of Generative Adversarial Networks (GANs) has garnered significant interest in the realm of image demosaicking, owing to their ability to generate intricate details. However, when demosaicing mosaic images in remote sensing techniques, GANs, although capable of generating rich details, often introduce unpleasant artifacts while generating content. To address this challenge and differentiate between undesirable artifacts and realistic details, we have devised a novel framework based on a Progressive Discrimination Strategy within a Generative Adversarial Network architecture for image demosaicking. Our approach incorporates an artifact-weighted Location Map refinement technique to guide the optimization process towards generating authentic details in a stable and precise manner. Furthermore, our framework integrates a global attention mechanism to boost the interaction of spatial-channel information across different dimensions, thereby enhancing the performance of the generator network. Moreover, we conduct a comparative analysis of various prevalent attention mechanisms in the context of remote sensing image demosaicking. The experimental findings unequivocally demonstrate that our proposed methodology not only achieves superior reconstruction accuracy on the dataset but also enhances the perceptual quality of the generated images. By effectively mitigating artifacts and emphasizing the generation of true details, our approach represents a significant advancement in the field of remote sensing image demosaicking, promising enhanced visual fidelity and realism in reconstructed images.<\/jats:p>","DOI":"10.3390\/rs16132283","type":"journal-article","created":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T05:16:18Z","timestamp":1719206178000},"page":"2283","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Towards a Novel Generative Adversarial Network-Based Framework for Remote Sensing Image Demosaicking"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1665-1726","authenticated-orcid":false,"given":"Yuxuan","family":"Guo","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China"},{"name":"Institute of Aerospace Science and Technology, Wuhan University, Wuhan 430072, China"}]},{"given":"Xuemin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China"},{"name":"Institute of Aerospace Science and Technology, Wuhan University, Wuhan 430072, China"},{"name":"Hubei Luojia Laboratory, Wuhan 430070, China"}]},{"given":"Guang","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China"},{"name":"Institute of Aerospace Science and Technology, Wuhan University, Wuhan 430072, China"},{"name":"Hubei Luojia Laboratory, Wuhan 430070, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1109\/TCE.2004.1277885","article-title":"Effective use of spatial and spectral correlations for color filter array demosaicking","volume":"50","author":"Chang","year":"2004","journal-title":"IEEE Trans. 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