{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T06:20:07Z","timestamp":1770963607503,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T00:00:00Z","timestamp":1770768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82502451, 82272075, 62462017"],"award-info":[{"award-number":["82502451, 82272075, 62462017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Guangxi Natural Science Foundation","award":["2025GXNSFBA069390"],"award-info":[{"award-number":["2025GXNSFBA069390"]}]},{"name":"the Guangxi Regional Innovation Capacity Improvement Program","award":["XT2503960034"],"award-info":[{"award-number":["XT2503960034"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Single-image super-resolution (SISR) is an essential low-level visual task that aims to produce high-resolution images from low-resolution inputs. However, most existing SISR methods heavily rely on ideal degradation kernels and rarely consider the actual noise distribution. To tackle these issues, this paper presents a progressive upsampling generative adversarial network with collaborative attention mechanism called PUGAN. Specifically, the residual multiscale blocks (RMBs) based on stacked mixed-pooling multiscale structures (MPMSs) is designed to make full use of multiscale global\u2013local hierarchical features, and the frequency collaborative attention mechanism (CAM) is used to fully dig up high- and low-frequency characteristics. Meanwhile, we design a progressive upsampling strategy to guide the model\u2019s learning better while reducing the model\u2019s complexity. Finally, the discriminator is also used to evaluate the reconstructed high-resolution images for balancing super-resolution reconstruction and detail enhancement. Our PUGAN can yield comparable PSNR\/SSIM\/LPIPS values for the NTIRE 2020, Urban 100, and B100 datasets, whose values are 33.987\/0.9673\/0.1210, 32.966\/0.9483\/0.1431, and 33.627\/0.9546\/0.1354 for the scale factor of \u00d72 as well as 26.349\/0.8721\/0.1975, 26.110\/0.8614\/0.1983, and 26.306\/0.8803\/0.1978 for the scale factor of \u00d74, respectively. Extensive experiments demonstrate that our PUGAN outperforms state-of-the-art SISR methods in qualitative and quantitative assessments for the SISR task. Additionally, our PUGAN shows the potential benefits to pathological image super-resolution.<\/jats:p>","DOI":"10.3390\/jimaging12020079","type":"journal-article","created":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T15:28:11Z","timestamp":1770910091000},"page":"79","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Progressive Upsampling Generative Adversarial Network with Collaborative Attention for Single-Image Super-Resolution"],"prefix":"10.3390","volume":"12","author":[{"given":"Haoxiang","family":"Lu","sequence":"first","affiliation":[{"name":"Guangdong Cardiovascular Institute, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Sciences, Guangzhou 510080, China"},{"name":"Department of Radiology, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou 510080, China"},{"name":"Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China"},{"name":"School of Computer and Information Security, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Business, Guilin Institute of Information Technology, Guilin 541100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengyuan","family":"Jing","sequence":"additional","affiliation":[{"name":"Guangdong Cardiovascular Institute, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Sciences, Guangzhou 510080, China"},{"name":"Department of Radiology, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou 510080, China"},{"name":"Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziming","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Security, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Security, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"110935","DOI":"10.1016\/j.patcog.2024.110935","article-title":"A review of deep-learning-based super-resolution: From methods to applications","volume":"157","author":"Su","year":"2025","journal-title":"Pattern Recognit."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yue, Z., Liao, K., and Loy, C.C. 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