{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T00:56:47Z","timestamp":1768352207196,"version":"3.49.0"},"reference-count":62,"publisher":"Association for Computing Machinery (ACM)","issue":"1s","license":[{"start":{"date-parts":[[2021,1,31]],"date-time":"2021-01-31T00:00:00Z","timestamp":1612051200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Hunan Science and Technology Planning Project","award":["2019RS3019"],"award-info":[{"award-number":["2019RS3019"]}]},{"name":"Hunan Provincial Natural Science Foundation of China for Distinguished Young Scholars","award":["2018JJ1025"],"award-info":[{"award-number":["2018JJ1025"]}]},{"name":"Hunan Province Science and Technology Project Funds","award":["2018TP1036"],"award-info":[{"award-number":["2018TP1036"]}]},{"name":"Hunan General project of Education Department","award":["19C1758"],"award-info":[{"award-number":["19C1758"]}]},{"name":"Ph.D. Research Startup Foundation of Xiangtan University","award":["19QDZ57"],"award-info":[{"award-number":["19QDZ57"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2021,1,31]]},"abstract":"<jats:p>Single image super-resolution attempts to reconstruct a high-resolution (HR) image from its corresponding low-resolution (LR) image, which has been a research hotspot in computer vision and image processing for decades. To improve the accuracy of super-resolution images, many works adopt very deep networks to model the translation from LR to HR, resulting in memory and computation consumption. In this article, we design a lightweight dense connection distillation network by combining the feature fusion units and dense connection distillation blocks (DCDB) that include selective cascading and dense distillation components. The dense connections are used between and within the distillation block, which can provide rich information for image reconstruction by fusing shallow and deep features. In each DCDB, the dense distillation module concatenates the remaining feature maps of all previous layers to extract useful information, the selected features are then assessed by the proposed layer contrast-aware channel attention mechanism, and finally the cascade module aggregates the features. The distillation mechanism helps to reduce training parameters and improve training efficiency, and the layer contrast-aware channel attention further improves the performance of model. The quality and quantity experimental results on several benchmark datasets show the proposed method performs better tradeoff in term of accuracy and efficiency.<\/jats:p>","DOI":"10.1145\/3414838","type":"journal-article","created":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T01:53:55Z","timestamp":1617242035000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Lightweight Single Image Super-resolution with Dense Connection Distillation Network"],"prefix":"10.1145","volume":"17","author":[{"given":"Yanchun","family":"Li","sequence":"first","affiliation":[{"name":"Department of Computer and Information Engineering, Ajou University, South Korea Institute of Management and Information Technologies, Chiba University, Japan"}]},{"given":"Jianglian","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Engineering, Ajou University, South Korea Institute of Management and Information Technologies, Chiba University, Japan"}]},{"given":"Zhetao","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Engineering, Ajou University, South Korea Institute of Management and Information Technologies, Chiba University, JapanSchool of Computer Science, Xiangtan University, China"}]},{"given":"Sangyoon","family":"Oh","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Engineering, Ajou University, South Korea Institute of Management and Information Technologies, Chiba University, Japan"}]},{"given":"Nobuyoshi","family":"Komuro","sequence":"additional","affiliation":[{"name":"Institute of Management and Information Technologies, Chiba University, Inage-ku, Chiba, Japan"}]}],"member":"320","published-online":{"date-parts":[[2021,3,31]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01249-6_16"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the International Conference on Image Processing.","author":"Wong P. W.","year":"1996","unstructured":"Wong P. W. Allebach J. 1996 . Edge-directed interpolation . In Proceedings of the International Conference on Image Processing. Wong P. W. Allebach J. 1996. Edge-directed interpolation. In Proceedings of the International Conference on Image Processing."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.5244\/C.26.135"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0450(1979)018<1016:LFIOAT>2.0.CO;2"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01132"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2439281"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"e_1_2_1_8_1","volume-title":"A learned representation for artistic style. CoRR abs\/1610.07629","author":"Dumoulin Vincent","year":"2017","unstructured":"Vincent Dumoulin , Jonathon Shlens , and Manjunath Kudlur . 2017. A learned representation for artistic style. CoRR abs\/1610.07629 ( 2017 ). Vincent Dumoulin, Jonathon Shlens, and Manjunath Kudlur. 2017. A learned representation for artistic style. CoRR abs\/1610.07629 (2017)."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.5555\/3367032.3367139"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00179"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_1_12_1","article-title":"Squeeze-and-excitation networks","volume":"142","author":"Hu J.","year":"2019","unstructured":"J. Hu , L. Shen , S. Albanie , G. Sun , and E. Wu . 2019 . Squeeze-and-excitation networks . IEEE Trans. Pattern Anal. Mach. Intell. 142 , 8 (2019). J. Hu, L. Shen, S. Albanie, G. Sun, and E. Wu. 2019. Squeeze-and-excitation networks. IEEE Trans. Pattern Anal. Mach. Intell. 142, 8 (2019).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"e_1_2_1_13_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201917)","author":"Huang Gao","unstructured":"Gao Huang , Zhuang Liu , and Kilian Q. Weinberger . 2017. Densely connected convolutional networks . In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201917) . Gao Huang, Zhuang Liu, and Kilian Q. Weinberger. 2017. Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201917)."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3351084"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00082"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICTSD.2015.7095900"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.730558"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.182"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.181"},{"key":"e_1_2_1_21_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba . 2015 . Adam : A method for stochastic optimization. CoRR abs\/1412.6980 (2015). Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. CoRR abs\/1412.6980 (2015)."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.618"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.19"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2006.877407"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.151"},{"key":"e_1_2_1_26_1","volume-title":"Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW\u201917)","author":"Lim B.","unstructured":"B. Lim , S. Son , H. Kim , S. Nah , and K. M. Lee . 2017. Enhanced deep residual networks for single image super-resolution . In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW\u201917) . 1132\u20131140. B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee. 2017. Enhanced deep residual networks for single image super-resolution. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW\u201917). 1132\u20131140."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.5555\/3157382.3157412"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2001.937655"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-016-4020-z"},{"key":"e_1_2_1_30_1","volume-title":"Proceedings of the International Conference on Machine Learning (ICML).","author":"Parmar Niki","year":"2018","unstructured":"Niki Parmar , Ashish Vaswani , Jakob Uszkoreit , Lukasz Kaiser , Noam Shazeer , and Alexander Ku . 2018 . Image transformer . In Proceedings of the International Conference on Machine Learning (ICML). Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, and Alexander Ku. 2018. Image transformer. In Proceedings of the International Conference on Machine Learning (ICML)."},{"key":"e_1_2_1_31_1","unstructured":"Adam Paszke Sam Gross and Adam Lerer. 2017. Automatic differentiation in pytorch.  Adam Paszke Sam Gross and Adam Lerer. 2017. Automatic differentiation in pytorch."},{"key":"e_1_2_1_32_1","volume-title":"A deep reinforced model for abstractive summarization. CoRR abs\/1705.04304","author":"Paulus Romain","year":"2018","unstructured":"Romain Paulus , Caiming Xiong , and Richard Socher . 2018. A deep reinforced model for abstractive summarization. CoRR abs\/1705.04304 ( 2018 ). Romain Paulus, Caiming Xiong, and Richard Socher. 2018. A deep reinforced model for abstractive summarization. CoRR abs\/1705.04304 (2018)."},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.481"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.207"},{"key":"e_1_2_1_35_1","volume-title":"Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201916). 1874","author":"Shi W.","year":"1883","unstructured":"W. Shi , J. Caballero , F. Husz\u00e1r , J. Totz , A. P. Aitken , R. Bishop , D. Rueckert , and Z. Wang . 2016. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network . In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201916). 1874 \u2013 1883 . W. Shi, J. Caballero, F. Husz\u00e1r, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang. 2016. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201916). 1874\u20131883."},{"key":"e_1_2_1_36_1","volume-title":"Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201908)","author":"Sun Jian","year":"2008","unstructured":"Jian Sun , Zongben Xu , and Heung-Yeung Shum . 2008 . Image super-resolution using gradient profile prior . In Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201908) . 1\u20138. Jian Sun, Zongben Xu, and Heung-Yeung Shum. 2008. Image super-resolution using gradient profile prior. In Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201908). 1\u20138."},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.298"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.486"},{"key":"e_1_2_1_39_1","volume-title":"NTIRE 2017 challenge on single image super-resolution: Methods and results. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201917) Workshops. 1110\u20131121","author":"Timofte Radu","year":"2017","unstructured":"Radu Timofte , Eirikur Agustsson , Luc Van Gool , Ming-Hsuan Yang , and Lei Zhang . 2017 . NTIRE 2017 challenge on single image super-resolution: Methods and results. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201917) Workshops. 1110\u20131121 . Radu Timofte, Eirikur Agustsson, Luc Van Gool, Ming-Hsuan Yang, and Lei Zhang. 2017. NTIRE 2017 challenge on single image super-resolution: Methods and results. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201917) Workshops. 1110\u20131121."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.241"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.514"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295349"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2012.2187181"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2012.2185041"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"e_1_2_1_47_1","unstructured":"Yang Wang. 2020. Survey on deep multi-modal data analytics: Collaboration rivalry and fusion. arXiv abs\/2006.08159. Retrieved from https:\/\/arxiv.org\/abs\/2006.08159.  Yang Wang. 2020. Survey on deep multi-modal data analytics: Collaboration rivalry and fusion. arXiv abs\/2006.08159. Retrieved from https:\/\/arxiv.org\/abs\/2006.08159."},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2655449"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.5555\/2919332.2919871"},{"key":"e_1_2_1_50_1","volume-title":"SRPGAN: Perceptual generative adversarial network for single image super resolution. CoRR abs\/1712.05927","author":"Wu Bingzhe","year":"2017","unstructured":"Bingzhe Wu , Haodong Duan , Zhichao Liu , and Guangyu Sun . 2017 . SRPGAN: Perceptual generative adversarial network for single image super resolution. CoRR abs\/1712.05927 (2017). Bingzhe Wu, Haodong Duan, Zhichao Liu, and Guangyu Sun. 2017. SRPGAN: Perceptual generative adversarial network for single image super resolution. CoRR abs\/1712.05927 (2017)."},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2018.2877886"},{"key":"e_1_2_1_52_1","unstructured":"L. Wu Y. Wang J. Gao M. Wang Z. Zha and D. Tao. 2020. Deep coattention-based comparator for relative representation learning in person re-identification. IEEE Trans. Neural Netw. Learn. Syst. (2020) 1\u201314.  L. Wu Y. Wang J. Gao M. Wang Z. Zha and D. Tao. 2020. Deep coattention-based comparator for relative representation learning in person re-identification. IEEE Trans. Neural Netw. Learn. Syst. (2020) 1\u201314."},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2018.2813971"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2878970"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2940684"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-27413-8_47"},{"key":"e_1_2_1_57_1","volume-title":"Self-attention generative adversarial networks. CoRR abs\/1805.08318","author":"Zhang Han","year":"2018","unstructured":"Han Zhang , Ian J. Goodfellow , Dimitris N. Metaxas , and Augustus Odena . 2018. Self-attention generative adversarial networks. CoRR abs\/1805.08318 ( 2018 ). Han Zhang, Ian J. Goodfellow, Dimitris N. Metaxas, and Augustus Odena. 2018. Self-attention generative adversarial networks. CoRR abs\/1805.08318 (2018)."},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2012.2208977"},{"key":"e_1_2_1_59_1","volume-title":"Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 3262\u20133271","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 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 3262\u20133271 . Kai Zhang, Wangmeng Zuo, and Lei Zhang. 2017. Learning a single convolutional super-resolution network for multiple degradations. In Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 3262\u20133271."},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00262"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2011.2162423"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3414838","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3414838","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:23:45Z","timestamp":1750202625000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3414838"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,31]]},"references-count":62,"journal-issue":{"issue":"1s","published-print":{"date-parts":[[2021,1,31]]}},"alternative-id":["10.1145\/3414838"],"URL":"https:\/\/doi.org\/10.1145\/3414838","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,31]]},"assertion":[{"value":"2020-04-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-07-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-03-31","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}