{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T05:07:04Z","timestamp":1735016824871,"version":"3.32.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685694","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,20]]},"abstract":"<jats:p>Satellite image transmission is crucial for distant sea monitoring and navigation safety. Beidou satellite communication has low cost, high security and confidentiality, and has broad development potential in the field of satellite image transmission. However, the communication capacity of Beidou satellites is limited. In order to increase the transmission rate of images, the images are compressed and sent. The shore end receives and decodes images with lower resolution. Therefore, the shore end designs super-resolution reconstruction technology to perform image processing on the received images. The improvement of resolution, improving the quality and texture details of images and making them clearer and more realistic are urgent problems that need to be solved. This paper proposes a generative adversarial network super-resolution reconstruction method based on an adaptive mechanism. This method uses a new perceptual loss function combination to integrate pixel loss, feature loss based on an adaptive weight mechanism, and adversarial loss to achieve a total loss function. At the same time, an adaptive weight mechanism is introduced in the feature loss, and a spatial attention mechanism is introduced in the generator and discriminator of SRGAN, thereby effectively distinguishing the salient areas and background areas of the image, and fully retaining the high-frequency details and edge features of the image. This solves the problems of low resolution, edge smoothness and detail distortion of images received at the shore end. Experimental results show that the image details of the overall area of the ship image (including the salient area and the background area) reconstructed by this method are clearer than existing super-resolution reconstruction methods such as EnhanceNet, ESPCN, and SRCNN, and the edge integrity of the salient area is better.<\/jats:p>","DOI":"10.3233\/faia241445","type":"book-chapter","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T09:49:02Z","timestamp":1734947342000},"source":"Crossref","is-referenced-by-count":0,"title":["Super-Resolution Reconstruction Technology of Beidou Satellite Transmission Images Based on Adaptive Mechanism"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2683-2732","authenticated-orcid":false,"given":"Lanyong","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Yuanlin","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Sheng","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining X"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA241445","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T09:49:03Z","timestamp":1734947343000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA241445"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,20]]},"ISBN":["9781643685694"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia241445","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,20]]}}}