{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:46:32Z","timestamp":1783611992158,"version":"3.55.0"},"reference-count":40,"publisher":"World Scientific Pub Co Pte Ltd","issue":"16","funder":[{"name":"Hainan Provincial Key Research and Development Program","award":["ZDYF2020018"],"award-info":[{"award-number":["ZDYF2020018"]}]},{"name":"Hainan Provincial Natural Science Foundation of China","award":["2019RC100"],"award-info":[{"award-number":["2019RC100"]}]},{"name":"Haikou key research and development program","award":["2020-049"],"award-info":[{"award-number":["2020-049"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2022,11,15]]},"abstract":"<jats:p> Existing super-resolution methods convert high-resolution images into low-resolution images, and use the synthesized images as input to train the model. However, it is difficult for synthetic low-resolution images to reflect the characteristics of real low-resolution images, resulting in poor model performance in practical applications. To address this problem, we propose a recurrent super-resolution framework, which consists of a degradation model and a reconstruction model. The degradation model degenerates the real high-resolution image into a more real low-resolution image, which is used as the input of the super-resolution reconstruction network, and then uses the reconstruction model to reconstruct the low-resolution image, and calculates the error with the original image. The generated high-resolution image is input into the degradation model again for degradation processing, forming a symmetrical and cyclic network structure, so that the super-resolution model has a better effect when reconstructing the real low-scoring image. In addition, the spatial attention mechanism is introduced into the generator network, which expands the receptive field of the convolution kernel, better extracts long-distance image features and improves the texture details of super-resolution images, which is consistent with the global. <\/jats:p>","DOI":"10.1142\/s0218126622502838","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T14:35:19Z","timestamp":1655476519000},"source":"Crossref","is-referenced-by-count":5,"title":["Image Super-Resolution Method Based on Dual Learning"],"prefix":"10.1142","volume":"31","author":[{"given":"Zhao","family":"Qiu","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Hainan University, Haikou, Hainan 570228, P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyu","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Haikou Hospital of the Maternal and Child Health, Haikou, Hainan, P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7091-7119","authenticated-orcid":false,"given":"Lihao","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Hainan University, Haikou, Hainan 570228, P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiale","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Hainan University, Haikou, Hainan 570228, P. R. 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