{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T06:03:55Z","timestamp":1732341835263,"version":"3.28.0"},"reference-count":27,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Sciences"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1016\/j.ins.2024.121612","type":"journal-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T22:38:51Z","timestamp":1730759931000},"page":"121612","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Wavelet structure-texture-aware super-resolution for pedestrian detection"],"prefix":"10.1016","volume":"691","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-4599-0744","authenticated-orcid":false,"given":"Wei-Yen","family":"Hsu","sequence":"first","affiliation":[]},{"given":"Chun-Hsiang","family":"Wu","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.ins.2024.121612_b0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2024.120576","article-title":"PIAENet: pyramid integration and attention enhanced network for object detection","volume":"670","author":"Tang","year":"2024","journal-title":"Inf. Sci."},{"key":"10.1016\/j.ins.2024.121612_b0010","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.ins.2020.06.023","article-title":"Cross-attentional bracket-shaped convolutional network for semantic image segmentation","volume":"539","author":"Hua","year":"2020","journal-title":"Inf. Sci."},{"key":"10.1016\/j.ins.2024.121612_b0015","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1016\/j.ins.2017.10.042","article-title":"Face recognition with a small occluded training set using spatial and statistical pooling","volume":"430\u2013431","author":"Long","year":"2018","journal-title":"Inf. Sci."},{"issue":"1\u201312","key":"10.1016\/j.ins.2024.121612_b0020","first-page":"5000212","article-title":"A dual transformer super-resolution network for improving the definition of vibration image","volume":"72","author":"Zhu","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"1\u201314","key":"10.1016\/j.ins.2024.121612_b0025","first-page":"5007614","article-title":"Structure-aware deep networks and pixel-level generative adversarial training for single image super-resolution","volume":"72","author":"Shi","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.ins.2024.121612_b0030","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.ins.2020.10.002","article-title":"Online multiple pedestrians tracking using deep temporal appearance matching association","volume":"561","author":"Yoon","year":"2021","journal-title":"Inf. Sci."},{"key":"10.1016\/j.ins.2024.121612_b0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2023.03.145","article-title":"GaitAMR: Cross-view gait recognition via aggregated multi-feature representation","volume":"636","author":"Chen","year":"2023","journal-title":"Inf. Sci."},{"key":"10.1016\/j.ins.2024.121612_b0040","doi-asserted-by":"crossref","first-page":"47780","DOI":"10.1109\/ACCESS.2018.2867586","article-title":"Residual super-resolution single shot network for low-resolution object detection","volume":"6","author":"Zhao","year":"2018","journal-title":"IEEE Access"},{"key":"10.1016\/j.ins.2024.121612_b0045","doi-asserted-by":"crossref","first-page":"4521","DOI":"10.1109\/ACCESS.2018.2793306","article-title":"Vehicle-to-vehicle distance estimation using a low-resolution camera based on visible light communications","volume":"6","author":"Tram","year":"2018","journal-title":"IEEE Access"},{"key":"10.1016\/j.ins.2024.121612_b0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.107846","article-title":"Pedestrian detection with super-resolution reconstruction for low-quality image","volume":"115","author":"Jin","year":"2021","journal-title":"Pattern Recogn."},{"key":"10.1016\/j.ins.2024.121612_b0055","doi-asserted-by":"crossref","unstructured":"C. Ledig et al., \u201cPhoto-realistic single image super-resolution using a generative adversarial network,\u201d presented at the Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, Hawaii,United States, 2017.","DOI":"10.1109\/CVPR.2017.19"},{"key":"10.1016\/j.ins.2024.121612_b0060","first-page":"91","article-title":"Faster r-cnn: towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2016","journal-title":"Adv. Neural Inf. Proces. Syst."},{"issue":"9","key":"10.1016\/j.ins.2024.121612_b0065","doi-asserted-by":"crossref","first-page":"1432","DOI":"10.3390\/rs12091432","article-title":"Small-object detection in remote sensing images with end-to-end edge-enhanced GAN and object detector network","volume":"12","author":"Rabbi","year":"2020","journal-title":"Remote Sens. (Basel)"},{"key":"10.1016\/j.ins.2024.121612_b0070","doi-asserted-by":"crossref","unstructured":"Luo, Ziwei, et al. \u201cDeep constrained least squares for blind image super-resolution,\u201d in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2022.","DOI":"10.1109\/CVPR52688.2022.01712"},{"issue":"2","key":"10.1016\/j.ins.2024.121612_b0075","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image super-resolution using deep convolutional networks","volume":"38","author":"Dong","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Machine Intell."},{"key":"10.1016\/j.ins.2024.121612_b0080","doi-asserted-by":"crossref","unstructured":"Y. Zhang, Y. Tian, Y. Kong, B. Zhong, Y. Fu, \u201cResidual dense network for image super-resolution,\u201d presented at the Proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City, Utah, United States, 2018.","DOI":"10.1109\/CVPR.2018.00262"},{"key":"10.1016\/j.ins.2024.121612_b0085","doi-asserted-by":"crossref","unstructured":"Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, Y. Fu, \u201cImage super-resolution using very deep residual channel attention networks,\u201d presented at the Proceedings of the European conference on computer vision (ECCV), Munich, Germany, 2018.","DOI":"10.1007\/978-3-030-01234-2_18"},{"issue":"6","key":"10.1016\/j.ins.2024.121612_b0090","doi-asserted-by":"crossref","first-page":"3512","DOI":"10.1109\/TGRS.2018.2885506","article-title":"Achieving super-resolution remote sensing images via the wavelet transform combined with the recursive res-net","volume":"57","author":"Ma","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"11","key":"10.1016\/j.ins.2024.121612_b0095","doi-asserted-by":"crossref","first-page":"2599","DOI":"10.1109\/TPAMI.2018.2865304","article-title":"Fast and accurate image super-resolution with deep laplacian pyramid networks","volume":"41","author":"Lai","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.ins.2024.121612_b0100","doi-asserted-by":"crossref","unstructured":"Z. Min, M. Ying, S. Dihua. \u201cTunnel pedestrian detection based on super resolution and convolutional neural network,\u201d presented at the 2019 Chinese Control And Decision Conference (CCDC), Nanchang, China, 2019.","DOI":"10.1109\/CCDC.2019.8833181"},{"key":"10.1016\/j.ins.2024.121612_b0105","doi-asserted-by":"crossref","unstructured":"R. Girshick. \u201cFast r-cnn,\u201d presented at the Proceedings of the IEEE international conference on computer vision, Boston, Massachusetts, United States, 2015.","DOI":"10.1109\/ICCV.2015.169"},{"key":"10.1016\/j.ins.2024.121612_b0110","doi-asserted-by":"crossref","unstructured":"Y. Chen et al. \u201cDrop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution,\u201d in Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, 3435-3444.","DOI":"10.1109\/ICCV.2019.00353"},{"key":"10.1016\/j.ins.2024.121612_b0115","doi-asserted-by":"crossref","unstructured":"B. Lim, S. Son, H. Kim, S. Nah, K. Mu Lee. \u201cEnhanced deep residual networks for single image super-resolution,\u201d in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, Honolulu, Hawaii, United States, 2017, 136-144.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"10.1016\/j.ins.2024.121612_b0120","series-title":"European conference on computer vision","first-page":"740","article-title":"Microsoft coco: Common objects in context","author":"Lin","year":"2014"},{"issue":"2","key":"10.1016\/j.ins.2024.121612_b0125","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (voc) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"issue":"1\u201311","key":"10.1016\/j.ins.2024.121612_b0130","first-page":"5615611","article-title":"Transformer-based multistage enhancement for remote sensing image super-resolution","volume":"60","author":"Lei","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.ins.2024.121612_b0135","doi-asserted-by":"crossref","unstructured":"M. Taiana, J.C. Nascimento, A. Bernardino. \u201cAn improved labelling for the INRIA person data set for pedestrian detection,\u201d presented at the Iberian Conference on Pattern Recognition and Image Analysis, Madeira, Portugal, 2013.","DOI":"10.1007\/978-3-642-38628-2_34"}],"container-title":["Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025524015263?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025524015263?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T02:58:02Z","timestamp":1732330682000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0020025524015263"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2]]},"references-count":27,"alternative-id":["S0020025524015263"],"URL":"https:\/\/doi.org\/10.1016\/j.ins.2024.121612","relation":{},"ISSN":["0020-0255"],"issn-type":[{"type":"print","value":"0020-0255"}],"subject":[],"published":{"date-parts":[[2025,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Wavelet structure-texture-aware super-resolution for pedestrian detection","name":"articletitle","label":"Article Title"},{"value":"Information Sciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ins.2024.121612","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"121612"}}