{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T22:52:43Z","timestamp":1768344763987,"version":"3.49.0"},"reference-count":69,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T00:00:00Z","timestamp":1675641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["51979085"],"award-info":[{"award-number":["51979085"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Science and Technology Project of Jiangxi Provincial Education Department","award":["GJJ212101 and GJJ219310"],"award-info":[{"award-number":["GJJ212101 and GJJ219310"]}]},{"name":"Nanchang Key Laboratory Construction Project","award":["2020-NCZDSY-005"],"award-info":[{"award-number":["2020-NCZDSY-005"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2023,5,31]]},"abstract":"<jats:p>The advent of convolutional neural networks (CNNs) has brought substantial progress in image super-resolution (SR) reconstruction. However, most SR methods pursue deep architectures to boost performance, and the resulting large model sizes are impractical for real-world applications. Furthermore, they insufficiently explore the internal structural information of image features, disadvantaging the restoration of fine texture details. To solve these challenges, we propose a lightweight architecture based on a CNN named attention-directed feature aggregation network (AFAN), consisting of chained stacking multi-aware attention modules (MAAMs) and a simple channel attention module (SCAM), for image SR. Specifically, in each MAAM, we construct a space-aware attention block (SAAB) and a dimension-aware attention block (DAAB) that individually yield unique three-dimensional modulation coefficients to adaptively recalibrate structural information from an asymmetric convolution residual block (ACRB). The synergistic strategy captures multiple content features that are both space-aware and dimension-aware to preserve more fine-grained details. In addition, to further enhance the accuracy and robustness of the network, SCAM is embedded in the last MAAM to highlight channels with high activated values at low computational load. Comprehensive experiments verify that our proposed network attains high qualitative accuracy while employing fewer parameters and moderate computational requirements, exceeding most state-of-the-art lightweight approaches.<\/jats:p>","DOI":"10.1145\/3546076","type":"journal-article","created":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T10:21:03Z","timestamp":1656584463000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Image Super-Resolution via Lightweight Attention-Directed Feature Aggregation Network"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2054-1392","authenticated-orcid":false,"given":"Li","family":"Wang","sequence":"first","affiliation":[{"name":"Hohai University, Jiangsu Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5228-4207","authenticated-orcid":false,"given":"Ke","family":"Li","sequence":"additional","affiliation":[{"name":"Nanchang Institute of Technology, Jiangxi Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5113-3389","authenticated-orcid":false,"given":"Jingjing","family":"Tang","sequence":"additional","affiliation":[{"name":"Hohai University, Jiangsu Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4116-4565","authenticated-orcid":false,"given":"Yuying","family":"Liang","sequence":"additional","affiliation":[{"name":"Nanchang Institute of Technology, Jiangxi Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,2,6]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"252","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV\u201918)","author":"Ahn Namhyuk","year":"2018","unstructured":"Namhyuk Ahn, Byungkon Kang, and Kyung Ah Sohn. 2018. Fast, accurate, and lightweight super-resolution with cascading residual network. In Proceedings of the European Conference on Computer Vision (ECCV\u201918). 252\u2013268."},{"key":"e_1_3_1_3_2","unstructured":"Supratik Banerjee Cagri Ozcinar Aakanksha Rana Aljosa Smolic and Michael Manzke. 2020. Sub-Pixel Back-Projection Network For Lightweight Single Image Super-Resolution. arXiv:2008.01116 (2020). https:\/\/arxiv.org\/abs\/2008.01116."},{"key":"e_1_3_1_4_2","first-page":"1","volume-title":"Proceedings of the 23rd British Machine Vision Conference (BMVC\u201912)","author":"Bevilacqua Marco","year":"2012","unstructured":"Marco Bevilacqua, Aline Roumy, Christine Guillemot, and Marie-line Alberi Morel. 2012. Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In Proceedings of the 23rd British Machine Vision Conference (BMVC\u201912). British Machine Vision Association, Surrey, 1\u201310."},{"key":"e_1_3_1_5_2","first-page":"391","volume-title":"European Conference on Computer Vision (ECCV\u201916)","author":"Chao D.","year":"2016","unstructured":"D. Chao, C. L. Chen, and X. Tang. 2016. Accelerating the super-resolution convolutional neural network. In European Conference on Computer Vision (ECCV\u201916). 391\u2013407."},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-66645-3_20"},{"key":"e_1_3_1_7_2","first-page":"1150","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW\u201917)","author":"Choi J.","year":"2017","unstructured":"J. Choi and M. Kim. 2017. A deep convolutional neural network with selection units for super-resolution. In IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW\u201917). 1150\u20131156."},{"key":"e_1_3_1_8_2","first-page":"59","volume-title":"International Conference on Pattern Recognition (ICPR\u201921)","author":"Chu Xiangxiang","year":"2021","unstructured":"Xiangxiang Chu, Bo Zhang, Hailong Ma, Ruijun Xu, and Qingyuan Li. 2021. Fast, accurate and lightweight super-resolution with neural architecture search. In International Conference on Pattern Recognition (ICPR\u201921). 59\u201364."},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-66823-5_6"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"e_1_3_1_11_2","article-title":"Feature distillation interaction weighting network for lightweight image super-resolution","volume":"2112","author":"Gao Guangwei","year":"2021","unstructured":"Guangwei Gao, Wenjie Li, Juncheng Li, Fei Wu, Huimin Lu, and Yi Yu. 2021. Feature distillation interaction weighting network for lightweight image super-resolution. CoRRw abs\/2112.08655 (2021).","journal-title":"CoRRw"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.811513"},{"key":"e_1_3_1_13_2","doi-asserted-by":"crossref","unstructured":"Meng-Hao Guo Tian-Xing Xu Jiang-Jiang Liu Zheng-Ning Liu Peng-Tao Jiang Tai-Jiang Mu Song-Hai Zhang Ralph R. Martin Ming-Ming Cheng and Shi-Min Hu. 2022. Attention mechanisms in computer vision: A survey. Computational Visual Media 8 3 (2022) 331\u2013368.","DOI":"10.1007\/s41095-022-0271-y"},{"key":"e_1_3_1_14_2","first-page":"1732","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR\u201919)","author":"He Xiangyu","year":"2019","unstructured":"Xiangyu He, Zitao Mo, Peisong Wang, Yang Liu, Mingyuan Yang, and Jian Cheng. 2019. ODE-inspired network design for single image super-resolution. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR\u201919). 1732\u20131741."},{"key":"e_1_3_1_15_2","first-page":"7132","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201917)","author":"Hu Jie","year":"2017","unstructured":"Jie Hu, Li Shen, Gang Sun, and Samuel Albanie. 2017. Squeeze-and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201917). 7132\u20137141."},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2019.2915238"},{"key":"e_1_3_1_17_2","first-page":"5179","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201915)","author":"Huang Jiabin","year":"2015","unstructured":"Jiabin Huang, Abhishek Singh, and Narendra Ahuja. 2015. Single image super-resolution from transformed self-exemplars. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201915). 5179\u20135206."},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3351084"},{"key":"e_1_3_1_19_2","first-page":"723","volume-title":"IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Hui Zheng","year":"2018","unstructured":"Zheng Hui, Xiumei Wang, and Xinbo Gao. 2018. Fast and accurate single image super-resolution via information distillation network. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 723\u2013731."},{"key":"e_1_3_1_20_2","first-page":"900","volume-title":"IEEE International Conference on Robotics and Automation (ICRA\u201920)","author":"Islam Md Jahidul","year":"2020","unstructured":"Md Jahidul Islam, Sadman Sakib Enan, Peigen Luo, and Junaed Sattar. 2020. Underwater image super-resolution using deep residual multipliers. In IEEE International Conference on Robotics and Automation (ICRA\u201920). 900\u2013906."},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBC.2020.2977513"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.182"},{"key":"e_1_3_1_23_2","first-page":"1637","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201916)","author":"Kim Jiwon","year":"2016","unstructured":"Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee. 2016. Deeply-recursive convolutional network for image super-resolution. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201916). 1637\u20131645."},{"key":"e_1_3_1_24_2","unstructured":"Jun Hyuk Kim Jun Ho Choi Manri Cheon and Jong Seok Lee. 2018. RAM: Residual Attention Module for Single Image Super-Resolution. arXiv: 1811.12043 (2018). https:\/\/arxiv.org\/abs\/1811.12043."},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.618"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.2970104"},{"key":"e_1_3_1_27_2","first-page":"4681","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201917)","author":"Ledig Christian","year":"2017","unstructured":"Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, and Wenzhe Shi. 2017. Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201917). 4681\u20134690."},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3014953"},{"key":"e_1_3_1_29_2","first-page":"517","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV\u201918)","author":"Li Juncheng","year":"2018","unstructured":"Juncheng Li, Faming Fang, Kangfu Mei, and Guixu Zhang. 2018. Multi-scale residual network for image super-resolution. In Proceedings of the European Conference on Computer Vision (ECCV\u201918). 517\u2013532."},{"key":"e_1_3_1_30_2","unstructured":"Zhuangzi Li. 2019. Image Super-Resolution Using Attention Based DenseNet with Residual Deconvolution. arXiv:1907.05282 (2019). http:\/\/arxiv.org\/abs\/1907.05282."},{"key":"e_1_3_1_31_2","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR\u201919)","author":"Li Zhen","year":"2019","unstructured":"Zhen Li, Jinglei Yang, Zheng Liu, Xiaomin Yang, Gwanggil Jeon, and Wei Wu. 2019. Feedback network for image super-resolution. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR\u201919)."},{"key":"e_1_3_1_32_2","first-page":"1132","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW\u201917)","author":"Lim Bee","year":"2017","unstructured":"Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee. 2017. Enhanced deep residual networks for single image super-resolution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW\u201917). 1132\u20131140."},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106103"},{"key":"e_1_3_1_34_2","first-page":"2356","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201920)","author":"Liu J.","year":"2020","unstructured":"J. Liu, W. Zhang, Y. Tang, J. Tang, and G. Wu. 2020. Residual feature aggregation network for image super-resolution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201920). 2356\u20132365."},{"key":"e_1_3_1_35_2","first-page":"1","article-title":"Cross-SRN: Structure-preserving super-resolution network with cross convolution","author":"Liu Yuqing","year":"2021","unstructured":"Yuqing Liu, Qi Jia, Xin Fan, Shanshe Wang, Siwei Ma, and Wen Gao. 2021. Cross-SRN: Structure-preserving super-resolution network with cross convolution. IEEE Transactions on Circuits and Systems for Video Technology (2021), 1\u20131.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3338533.3366558"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2021.3084522"},{"key":"e_1_3_1_38_2","first-page":"416","volume-title":"IEEE International Conference on Computer Vision (CVPR\u201902)","author":"Martin D.","year":"2002","unstructured":"D. Martin, C. Fowlkes, D. Tal, and J. Malik. 2002. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In IEEE International Conference on Computer Vision (CVPR\u201902). 416\u2013423."},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-016-4020-z"},{"key":"e_1_3_1_40_2","first-page":"3138","volume-title":"IEEE Winter Conference on Applications of Computer Vision (WACV\u201921)","author":"Misra Diganta","year":"2021","unstructured":"Diganta Misra, Trikay Nalamada, Ajay Uppili Arasanipalai, and Qibin Hou. 2021. Rotate to attend: Convolutional triplet attention module. In IEEE Winter Conference on Applications of Computer Vision (WACV\u201921). 3138\u20133147."},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2018.05.009"},{"key":"e_1_3_1_42_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TMM.2021.3134172","article-title":"Dynamic residual self-attention network for lightweight single image super-resolution","author":"Park Karam","year":"2021","unstructured":"Karam Park, Jae Woong Soh, and Nam Ik Cho. 2021. Dynamic residual self-attention network for lightweight single image super-resolution. IEEE Transactions on Multimedia (2021), 1\u20131.","journal-title":"IEEE Transactions on Multimedia"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-41778-3_18"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.207"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40760-4_2"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2021.1004009"},{"key":"e_1_3_1_47_2","first-page":"2818","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201916)","author":"Szegedy Christian","year":"2016","unstructured":"Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201916). 2818\u20132826."},{"key":"e_1_3_1_48_2","first-page":"3147","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201917)","author":"Tai Ying","year":"2017","unstructured":"Ying Tai, Jian Yang, and Xiaoming Liu. 2017. Image super-resolution via deep recursive residual network. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201917). 3147\u20133155."},{"key":"e_1_3_1_49_2","first-page":"4539","volume-title":"Proceedings of the IEEE International Conference on Computer Vision (ICCV\u201917)","author":"Tai Ying","year":"2017","unstructured":"Ying Tai, Jian Yang, Xiaoming Liu, and Chunyan Xu. 2017. MemNet: A persistent memory network for image restoration. In Proceedings of the IEEE International Conference on Computer Vision (ICCV\u201917). 4539\u20134547."},{"key":"e_1_3_1_50_2","doi-asserted-by":"crossref","unstructured":"Chunwei Tian Yong Xu Wangmeng Zuo Chia-Wen Lin and David Zhang. 2022. Asymmetric CNN for Image Superresolution. IEEE Transactions on Systems Man and Cybernetics: Systems 52 6 (2022) 3718\u20133730.","DOI":"10.1109\/TSMC.2021.3069265"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106235"},{"key":"e_1_3_1_52_2","first-page":"114","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW\u201917)","author":"Timofte Radu","year":"2017","unstructured":"Radu Timofte, Eirikur Agustsson, Luc Van Gool, Ming Hsuan Yang, and Qi Guo. 2017. NTIRE 2017 challenge on single image super-resolution: Methods and results. In IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW\u201917). 114\u2013125."},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBC.2020.3028356"},{"key":"e_1_3_1_54_2","unstructured":"Chaofeng Wang Zheng Li and Jun Shi. 2019. Lightweight Image Super-Resolution with Adaptive Weighted Learning Network. arXiv:1904.02358 (2019). https:\/\/arxiv.org\/abs\/1904.02358"},{"key":"e_1_3_1_55_2","doi-asserted-by":"crossref","unstructured":"Xuehui Wang Qing Wang Yuzhi Zhao Junchi Yan Lei Fan and Long Chen. 2020. Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature Learning. In Proceedings of the Asian Conference on Computer Vision (ACCV) . 268\u2013285.","DOI":"10.1007\/978-3-030-69532-3_17"},{"key":"e_1_3_1_56_2","first-page":"63","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV\u201919) Workshops","author":"Wang Xintao","year":"2019","unstructured":"Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy. 2019. ESRGAN: Enhanced super-resolution generative adversarial networks. In Proceedings of the European Conference on Computer Vision (ECCV\u201919) Workshops. 63\u201379."},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2021.103254"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"e_1_3_1_59_2","unstructured":"Huapeng Wu Jie Gui Jun Zhang James T. Kwok and Zhihui Wei. 2021. Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution. arXiv:2106.06996 (2021). https:\/\/arxiv.org\/abs\/2106.06996."},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-27413-8_47"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3009918"},{"key":"e_1_3_1_62_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2020.3045282"},{"key":"e_1_3_1_63_2","first-page":"3929","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201917)","author":"Zhang Kai","year":"2017","unstructured":"Kai Zhang, Wangmeng Zuo, Shuhang Gu, and Lei Zhang. 2017. Learning deep CNN denoiser prior for image restoration. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201917). 3929\u20133938."},{"key":"e_1_3_1_64_2","first-page":"3262","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201917)","author":"Zhang Kai","year":"2017","unstructured":"Kai Zhang, Wangmeng Zuo, and Lei Zhang. 2017. Learning a single convolutional super-resolution network for multiple degradations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201917). 3262\u20133271."},{"key":"e_1_3_1_65_2","doi-asserted-by":"crossref","unstructured":"Kai Zhang Wangmeng Zuo and Lei Zhang. 2019. Deep Plug-And-Play Super-Resolution for Arbitrary Blur Kernels. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . 1671\u20131681.","DOI":"10.1109\/CVPR.2019.00177"},{"key":"e_1_3_1_66_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"e_1_3_1_67_2","first-page":"2472","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201918)","author":"Zhang Yulun","year":"2018","unstructured":"Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu. 2018. Residual dense network for image super-resolution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201918). 2472\u20132481."},{"issue":"1","key":"e_1_3_1_68_2","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s11760-021-01969-4","article-title":"Attention-guided multi-path cross-CNN for underwater image super-resolution","volume":"16","author":"Zhang Yan","year":"2021","unstructured":"Yan Zhang, Shangxue Yang, Yemei Sun, Shudong Liu, and Xianguo Li. 2021. Attention-guided multi-path cross-CNN for underwater image super-resolution. Signal, Image and Video Processing 16, 1 (2021), 155\u2013163.","journal-title":"Signal, Image and Video Processing"},{"key":"e_1_3_1_69_2","doi-asserted-by":"crossref","unstructured":"Hengyuan Zhao Xiangtao Kong Jingwen He Yu Qiao and Chao Dong. 2020. Efficient Image Super-Resolution Using Pixel Attention. In Proceedings of the European Conference on Computer Vision (ECCV) Workshops . 56\u201372.","DOI":"10.1007\/978-3-030-67070-2_3"},{"key":"e_1_3_1_70_2","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV\u201919) Workshops","author":"Zhu Feiyang","year":"2019","unstructured":"Feiyang Zhu and Qijun Zhao. 2019. Efficient single image super-resolution via hybrid residual feature learning with compact back-projection network. In Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV\u201919) Workshops."}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3546076","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3546076","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:19Z","timestamp":1750188619000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3546076"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,6]]},"references-count":69,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,5,31]]}},"alternative-id":["10.1145\/3546076"],"URL":"https:\/\/doi.org\/10.1145\/3546076","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,6]]},"assertion":[{"value":"2022-01-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-06-23","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-02-06","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}