{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T17:18:58Z","timestamp":1740158338898,"version":"3.37.3"},"reference-count":21,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T00:00:00Z","timestamp":1616112000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2006228"],"award-info":[{"award-number":["U2006228"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Electrical and Computer Engineering"],"published-print":{"date-parts":[[2021,3,19]]},"abstract":"<jats:p>Most of the recent advances in image superresolution (SR) assume that the blur kernel during downsampling is predefined (e.g., Bicubic or Gaussian kernel), but it is a difficult task to make it suitable for all the realistic images. In this paper, we propose an Improved Superresolution Feedback Network (ISRFN) which is designed free to predefine the downsampling blur kernel by dealing with real-world HR-LR image pairs directly without downsampling process. We propose ISRFN by modifying the layers and network structures of the famous Superresolution Feedback Network (SRFBN). We trained the ISRFN with the Camera Lens Database named City100, which produced the HR and LR on the same lens, respectively, free for downsampling, so our proposed ISRFN is free to estimate the blur kernel. Due to different camera lens (smartphone and DSLR) databases, we perform two series of experiments under two camera lenses-based City100 databases, respectively, to choose the optimum network structures; experiments make it clear that different camera lens-based databases have different optimum network structures. We also compare our two ISRFNs with the state-of-the-art algorithms on performance; experiments show that our proposed ISRFN outperforms other state-of-the-art algorithms.<\/jats:p>","DOI":"10.1155\/2021\/5583620","type":"journal-article","created":{"date-parts":[[2021,3,20]],"date-time":"2021-03-20T18:50:12Z","timestamp":1616266212000},"page":"1-10","source":"Crossref","is-referenced-by-count":0,"title":["An Improved Feedback Network Superresolution on Camera Lens Images for Blind Superresolution"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6376-8963","authenticated-orcid":true,"given":"Yuhao","family":"Liu","sequence":"first","affiliation":[{"name":"College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ipr.2019.1438"},{"key":"2","first-page":"1","article-title":"Deep learning for image super-resolution: a survey","author":"Z. Wang","year":"2020","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"first-page":"1604","article-title":"Blind super-resolution with iterative kernel correction","author":"J. Gu","key":"3"},{"first-page":"1671","article-title":"Deep plug-and-play super-resolution for arbitrary blur kernels","author":"K. Zhang","key":"4"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107169"},{"first-page":"1652","article-title":"Camera lens super-resolution","author":"C. Chen","key":"6"},{"first-page":"1723","article-title":"Towards real scene super-resolution with raw images","author":"X. Xu","key":"7"},{"first-page":"3867","article-title":"Feedback network for image super-resolution","author":"Z. Li","key":"8"},{"first-page":"1","article-title":"Going deeper with convolutions","author":"C. Szegedy","key":"9"},{"first-page":"1646","article-title":"Accurate image super-resolution using very deep convolutional networks","author":"J. 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Tai","key":"21"}],"container-title":["Journal of Electrical and Computer Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2021\/5583620.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2021\/5583620.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2021\/5583620.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,20]],"date-time":"2021-03-20T18:50:17Z","timestamp":1616266217000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/jece\/2021\/5583620\/"}},"subtitle":[],"editor":[{"given":"Yang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,3,19]]},"references-count":21,"alternative-id":["5583620","5583620"],"URL":"https:\/\/doi.org\/10.1155\/2021\/5583620","relation":{},"ISSN":["2090-0155","2090-0147"],"issn-type":[{"type":"electronic","value":"2090-0155"},{"type":"print","value":"2090-0147"}],"subject":[],"published":{"date-parts":[[2021,3,19]]}}}