{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T11:05:22Z","timestamp":1770030322021,"version":"3.49.0"},"reference-count":18,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,9,15]]},"abstract":"<jats:p>In the field of super-resolution image reconstruction, as a learning-based method, deep plug-and-play super-resolution (DPSR) algorithm can be used to find the blur kernel by using the existing blind deblurring methods. However, DPSR is not flexible enough in processing images with high- and low-frequency information. Considering a channel attention mechanism can distinguish low-frequency information and features in low-resolution images, in this paper, we firstly introduce this mechanism and design a new residual channel attention networks (RCAN); then the RCAN is adopted to replace deep feature extraction part in DPSR to achieve the adaptive adjustment of channel characteristics. Through four test experiments based on Set5, Set14, Urban100 and BSD100 datasets, we find that, under different blur kernels and different scale factors, the average peak signal to noise ratio (PSNR) and structural similarity (SSIM) values of our proposed method increase by 0.31dB and 0.55%, respectively; under different noise levels, the average PSNR and SSIM values increase by 0.26dB and 0.51%, respectively.<\/jats:p>","DOI":"10.3233\/jifs-202696","type":"journal-article","created":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T14:29:46Z","timestamp":1628605786000},"page":"4069-4078","source":"Crossref","is-referenced-by-count":3,"title":["The enhanced deep Plug-and-play super-resolution algorithm with residual channel attention networks"],"prefix":"10.1177","volume":"41","author":[{"given":"Hongguang","family":"Pan","sequence":"first","affiliation":[{"name":"College of Electrical and Control Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an, P. R. China"}]},{"given":"Fan","family":"Wen","sequence":"additional","affiliation":[{"name":"College of Electrical and Control Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an, P. R. China"}]},{"given":"Xiangdong","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Electrical and Control Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an, P. R. China"}]},{"given":"Xinyu","family":"Lei","sequence":"additional","affiliation":[{"name":"College of Electrical and Control Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an, P. R. China"}]},{"given":"Xiaoling","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Electrical and Control Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an, P. R. China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-202696_ref1","doi-asserted-by":"crossref","unstructured":"Zhang K. , Zuo W. , Zhang L. , Learning a single convolutional super-resolution network for multipledegradations, in Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3262\u20133271, 2018.","DOI":"10.1109\/CVPR.2018.00344"},{"issue":"12","key":"10.3233\/JIFS-202696_ref2","doi-asserted-by":"crossref","first-page":"3106","DOI":"10.1109\/TMM.2019.2919431","article-title":"Deep learning for single image super-resolution: A briefreview","volume":"21","author":"Yang","year":"2019","journal-title":"IEEE Transactions on Multimedia"},{"issue":"99","key":"10.3233\/JIFS-202696_ref3","first-page":"1","article-title":"Deep learning for image super-resolution: A survey","volume":"PP","author":"Wang","year":"2020","journal-title":"IEEE Transactions onPattern Analysis and Machine Intelligence"},{"key":"10.3233\/JIFS-202696_ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.101877"},{"key":"10.3233\/JIFS-202696_ref5","doi-asserted-by":"crossref","unstructured":"Zhang K. , Zuo W. , Zhang L. , Deep plug-and-play super-resolution for arbitrary blur kernels, in Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1671\u20131681, 2019.","DOI":"10.1109\/CVPR.2019.00177"},{"key":"10.3233\/JIFS-202696_ref6","doi-asserted-by":"crossref","unstructured":"He K. , Zhang X. , Ren S. , Sun J. , Deep residual learning for image recognition, in Proceedings of the 2016IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778, 2016.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.3233\/JIFS-202696_ref7","doi-asserted-by":"crossref","unstructured":"Ledig C. , Theis L. , Husz\u00e1r F. , Caballero J. , Cunningham A. , Acosta A. , Aitken A. , Tejani A. , Totz J. , WangZ., et al., Photo-realistic single image super-resolution using a generative adversarial network, in Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4681\u20134690, 2017.","DOI":"10.1109\/CVPR.2017.19"},{"issue":"1","key":"10.3233\/JIFS-202696_ref8","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/0010-0285(80)90005-5","article-title":"A feature-integration theory of attention","volume":"12","author":"Treisman","year":"1980","journal-title":"Cognitive Psychology"},{"issue":"11","key":"10.3233\/JIFS-202696_ref9","doi-asserted-by":"crossref","first-page":"1715","DOI":"10.1364\/JOSAA.6.001715","article-title":"High-resolution image recovery from image-plane arrays, using convex projections","volume":"6","author":"Stark","year":"1989","journal-title":"Journal of The Optical Society of America A-optics Image Science and Vision"},{"key":"10.3233\/JIFS-202696_ref10","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s11571-019-09567-4","article-title":"An adaptive decoder design based on the receding horizon optimization in bmisystem","volume":"14","author":"Pan","year":"2020","journal-title":"Cognitive Neurodynamics"},{"key":"10.3233\/JIFS-202696_ref11","doi-asserted-by":"crossref","unstructured":"Zhang Y. , Li K. , Li K. , Wang L. , Zhong B. , Fu Y. , Image super-resolution using very deep residual channelattention networks, in Proceedings of the 2018 European Conference on Computer Vision (ECCV), pp.286\u2013301, 2018.","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"10.3233\/JIFS-202696_ref12","doi-asserted-by":"crossref","unstructured":"Cho T. , ParisS., HornS. 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