{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T05:45:54Z","timestamp":1757310354773,"version":"3.28.0"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T00:00:00Z","timestamp":1720483200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T00:00:00Z","timestamp":1720483200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s13042-024-02277-2","type":"journal-article","created":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T15:22:41Z","timestamp":1720538561000},"page":"5765-5780","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Joint features-guided linear transformer and CNN for efficient image super-resolution"],"prefix":"10.1007","volume":"15","author":[{"given":"Bufan","family":"Wang","sequence":"first","affiliation":[]},{"given":"Yongjun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Long","sequence":"additional","affiliation":[]},{"given":"Zhongwei","family":"Cui","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,9]]},"reference":[{"key":"2277_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3182809","volume":"60","author":"G Chen","year":"2022","unstructured":"Chen G, Jiao P, Hu Q, Xiao L, Ye Z (2022) Swinstfm: Remote sensing spatiotemporal fusion using swin transformer. IEEE Trans Geosci Remote Sens 60:1\u201318. https:\/\/doi.org\/10.1109\/TGRS.2022.3182809","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"2277_CR2","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.ins.2022.11.140","volume":"622","author":"C Wang","year":"2023","unstructured":"Wang C, Lv X, Shao M, Qian Y, Zhang Y (2023) A novel fuzzy hierarchical fusion attention convolution neural network for medical image super-resolution reconstruction. Inform Sci 622:424\u2013436. https:\/\/doi.org\/10.1016\/j.ins.2022.11.140","journal-title":"Inform Sci"},{"key":"2277_CR3","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2023.3238200","author":"R Ran","year":"2023","unstructured":"Ran R, Deng L-J, Jiang T-X, Hu J-F, Chanussot J, Vivone G (2023) Guidednet: a general cnn fusion framework via high-resolution guidance for hyperspectral image super-resolution. IEEE Trans Cybernet. https:\/\/doi.org\/10.1109\/TCYB.2023.3238200","journal-title":"IEEE Trans Cybernet"},{"issue":"12","key":"2277_CR4","doi-asserted-by":"publisher","first-page":"3322","DOI":"10.1109\/TIFS.2019.2916592","volume":"14","author":"Y Pang","year":"2019","unstructured":"Pang Y, Cao J, Wang J, Han J (2019) Jcs-net: Joint classification and super-resolution network for small-scale pedestrian detection in surveillance images. IEEE Trans Inform Forensics Secur 14(12):3322\u20133331. https:\/\/doi.org\/10.1109\/TIFS.2019.2916592","journal-title":"IEEE Trans Inform Forensics Secur"},{"issue":"2","key":"2277_CR5","doi-asserted-by":"publisher","first-page":"295","DOI":"10.48550\/arXiv.1501.00092","volume":"38","author":"C Dong","year":"2015","unstructured":"Dong C, Loy CC, He K, Tang X (2015) Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 38(2):295\u2013307. https:\/\/doi.org\/10.48550\/arXiv.1501.00092","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2277_CR6","doi-asserted-by":"publisher","unstructured":"Zhao H, Kong X, He J, Qiao Y, Dong C (2020) Efficient image super-resolution using pixel attention. In: Computer Vision\u2013ECCV 2020 Workshops: Glasgow, UK, August 23\u201328, 2020, Proceedings, Part III 16, pp. 56\u201372. Springer. https:\/\/doi.org\/10.48550\/arXiv.2010.01073","DOI":"10.48550\/arXiv.2010.01073"},{"key":"2277_CR7","doi-asserted-by":"publisher","unstructured":"Liang J, Cao J, Sun G, Zhang K, Van\u00a0Gool L, Timofte R (2021) Swinir: Image restoration using swin transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1833\u20131844. https:\/\/doi.org\/10.48550\/arXiv.2108.10257","DOI":"10.48550\/arXiv.2108.10257"},{"key":"2277_CR8","doi-asserted-by":"publisher","unstructured":"Lu Z, Li J, Liu H, Huang C, Zhang L, Zeng T (2022) Transformer for single image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 457\u2013466. https:\/\/doi.org\/10.48550\/arXiv.2108.11084","DOI":"10.48550\/arXiv.2108.11084"},{"key":"2277_CR9","doi-asserted-by":"publisher","unstructured":"Wu F, Fan A, Baevski A, Dauphin YN, Auli M (2019) Pay less attention with lightweight and dynamic convolutions. arXiv preprint arXiv:1901.10430. https:\/\/doi.org\/10.1145\/3340531.3412118","DOI":"10.1145\/3340531.3412118"},{"key":"2277_CR10","doi-asserted-by":"publisher","unstructured":"Lin X, Ma L, Liu W, Chang S-F (2020) Context-gated convolution. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XVIII 16, pp. 701\u2013718. Springer. https:\/\/doi.org\/10.48550\/arXiv.1910.05577","DOI":"10.48550\/arXiv.1910.05577"},{"key":"2277_CR11","doi-asserted-by":"publisher","unstructured":"Katharopoulos A, Vyas A, Pappas N, Fleuret F (2020) Transformers are rnns: Fast autoregressive transformers with linear attention. In: International Conference on Machine Learning, pp. 5156\u20135165. PMLR. https:\/\/doi.org\/10.48550\/arXiv.2006.16236","DOI":"10.48550\/arXiv.2006.16236"},{"key":"2277_CR12","doi-asserted-by":"publisher","unstructured":"Liu J, Pan Z, He H, Cai J, Zhuang B (2022) Ecoformer: Energy-saving attention with linear complexity. Adv Neural Inform Process Syst 35: 10295\u201310308. https:\/\/doi.org\/10.48550\/arXiv.2209.09004","DOI":"10.48550\/arXiv.2209.09004"},{"key":"2277_CR13","doi-asserted-by":"publisher","unstructured":"Lim B, Son S, Kim H, Nah S, Mu Lee K (2017) Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 136\u2013144. https:\/\/doi.org\/10.48550\/arXiv.1707.02921","DOI":"10.48550\/arXiv.1707.02921"},{"key":"2277_CR14","doi-asserted-by":"publisher","unstructured":"Xia B, Hang Y, Tian Y, Yang W, Liao Q, Zhou J (2022) Efficient non-local contrastive attention for image super-resolution. In: Proceedings of the AAAI conference on artificial intelligence, 36: 2759\u20132767 (2022) https:\/\/doi.org\/10.48550\/arXiv.2201.03794","DOI":"10.48550\/arXiv.2201.03794"},{"key":"2277_CR15","doi-asserted-by":"publisher","unstructured":"Chen Z, Zhang Y, Gu J, Kong L, Yang X, Yu F (2023) Dual aggregation transformer for image super-resolution. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 12312\u201312321. https:\/\/doi.org\/10.48550\/arXiv.2308.03364","DOI":"10.48550\/arXiv.2308.03364"},{"key":"2277_CR16","doi-asserted-by":"publisher","unstructured":"Ahn N, Kang B, Sohn K-A (2018) Fast, accurate, and lightweight super-resolution with cascading residual network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 252\u2013268. https:\/\/doi.org\/10.48550\/arXiv.1803.08664","DOI":"10.48550\/arXiv.1803.08664"},{"key":"2277_CR17","doi-asserted-by":"publisher","unstructured":"Hui Z, Gao X, Yang Y, Wang X (2019) Lightweight image super-resolution with information multi-distillation network. In: Proceedings of the 27th Acm International Conference on Multimedia, pp. 2024\u20132032. https:\/\/doi.org\/10.48550\/arXiv.1909.11856","DOI":"10.48550\/arXiv.1909.11856"},{"key":"2277_CR18","doi-asserted-by":"publisher","unstructured":"Liu J, Tang J, Wu G (2020) Residual feature distillation network for lightweight image super-resolution. In: Computer Vision\u2013ECCV 2020 Workshops: Glasgow, UK, August 23\u201328, 2020, Proceedings, Part III 16, pp. 41\u201355. Springer. https:\/\/doi.org\/10.48550\/arXiv.2009.11551","DOI":"10.48550\/arXiv.2009.11551"},{"key":"2277_CR19","doi-asserted-by":"publisher","unstructured":"Luo X, Xie Y, Zhang Y, Qu Y, Li C, Fu Y (2020) Latticenet: Towards lightweight image super-resolution with lattice block. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXII 16, pp. 272\u2013289. Springer. https:\/\/doi.org\/10.1007\/978-3-030-58542-6_17","DOI":"10.1007\/978-3-030-58542-6_17"},{"key":"2277_CR20","doi-asserted-by":"publisher","unstructured":"Wang X, Dong C, Shan Y (2022) Repsr: Training efficient vgg-style super-resolution networks with structural re-parameterization and batch normalization. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 2556\u20132564. https:\/\/doi.org\/10.1145\/3503161.3547915","DOI":"10.1145\/3503161.3547915"},{"key":"2277_CR21","doi-asserted-by":"publisher","unstructured":"Gao G, Li W, Li J, Wu F, Lu H, Yu Y (2022) Feature distillation interaction weighting network for lightweight image super-resolution. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 661\u2013669. https:\/\/doi.org\/10.48550\/arXiv.2112.08655","DOI":"10.48550\/arXiv.2112.08655"},{"issue":"4","key":"2277_CR22","doi-asserted-by":"publisher","first-page":"4826","DOI":"10.1109\/TPAMI.2022.3194090","volume":"45","author":"X Luo","year":"2023","unstructured":"Luo X, Qu Y, Xie Y, Zhang Y, Li C, Fu Y (2023) Lattice network for lightweight image restoration. IEEE Trans Pattern Anal Mach Intell 45(4):4826\u20134842. https:\/\/doi.org\/10.1109\/TPAMI.2022.3194090","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2277_CR23","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3268675","author":"H Wang","year":"2023","unstructured":"Wang H, Zhang Y, Qin C, Van Gool L, Fu Y (2023) Global aligned structured sparsity learning for efficient image super-resolution. IEEE Trans Pattern Anal Mach Intell. https:\/\/doi.org\/10.1109\/TPAMI.2023.3268675","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2277_CR24","doi-asserted-by":"publisher","unstructured":"Guo J, Zou X, Chen Y, Liu Y, Liu J, Yan Y, Hao J (2023) Asconvsr: Fast and lightweight super-resolution network with assembled convolutions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1582\u20131592. https:\/\/doi.org\/10.48550\/arXiv.2305.03387","DOI":"10.48550\/arXiv.2305.03387"},{"key":"2277_CR25","doi-asserted-by":"publisher","unstructured":"Muqeet A, Hwang J, Yang S, Kang J, Kim Y, Bae S-H (2020) Multi-attention based ultra lightweight image super-resolution. In: Computer Vision\u2013ECCV 2020 Workshops: Glasgow, UK, August 23\u201328, 2020, Proceedings, Part III 16, pp. 103\u2013118. Springer. https:\/\/doi.org\/10.48550\/arXiv.2008.12912","DOI":"10.48550\/arXiv.2008.12912"},{"key":"2277_CR26","doi-asserted-by":"publisher","unstructured":"Chen H, Gu J, Zhang Z (2021) Attention in attention network for image super-resolution. arXiv preprint arXiv:2104.09497. https:\/\/doi.org\/10.48550\/arXiv.2104.09497","DOI":"10.48550\/arXiv.2104.09497"},{"key":"2277_CR27","doi-asserted-by":"publisher","unstructured":"Zhang D, Li C, Xie N, Wang G, Shao J (2021) Pffn: Progressive feature fusion network for lightweight image super-resolution. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 3682\u20133690. https:\/\/doi.org\/10.1145\/3474085.3475650","DOI":"10.1145\/3474085.3475650"},{"key":"2277_CR28","doi-asserted-by":"publisher","unstructured":"Kong F, Li M, Liu S, Liu D, He J, Bai Y, Chen F, Fu L (2022) Residual local feature network for efficient super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 766\u2013776. https:\/\/doi.org\/10.48550\/arXiv.2205.07514","DOI":"10.48550\/arXiv.2205.07514"},{"key":"2277_CR29","doi-asserted-by":"publisher","unstructured":"Du Z, Liu D, Liu J, Tang J, Wu G, Fu L (2022) Fast and memory-efficient network towards efficient image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 853\u2013862. https:\/\/doi.org\/10.48550\/arXiv.2204.08397","DOI":"10.48550\/arXiv.2204.08397"},{"key":"2277_CR30","doi-asserted-by":"publisher","unstructured":"Fan C-M, Liu T-J, Liu K-H(2022) Sunet: Swin transformer unet for image denoising., 2333\u20132337. https:\/\/doi.org\/10.1109\/ISCAS48785.2022.9937486","DOI":"10.1109\/ISCAS48785.2022.9937486"},{"key":"2277_CR31","doi-asserted-by":"publisher","unstructured":"Tsai F-J, Peng Y-T, Lin Y-Y, Tsai C-C, Lin C-W (2022) Stripformer: Strip transformer for fast image deblurring. In: European Conference on Computer Vision, pp. 146\u2013162. Springer. https:\/\/doi.org\/10.48550\/arXiv.2204.04627","DOI":"10.48550\/arXiv.2204.04627"},{"key":"2277_CR32","unstructured":"Ma F, Sun J (2022) Crossuformer: A cross attention u-shape transformer for low light image enhancement. In: Proceedings of the Asian Conference on Computer Vision, pp. 928\u2013943"},{"key":"2277_CR33","doi-asserted-by":"publisher","unstructured":"Song Y, He Z, Qian H, Du X (2023) Vision transformers for single image dehazing. IEEE Transactions on Image Processing 32, 1927\u20131941. https:\/\/doi.org\/10.48550\/arXiv.2204.03883","DOI":"10.48550\/arXiv.2204.03883"},{"key":"2277_CR34","doi-asserted-by":"publisher","unstructured":"Chen, X., Wang, X., Zhou, J., Qiao, Y., Dong, C.: Activating more pixels in image super-resolution transformer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 22367\u201322377 (2023). https:\/\/doi.org\/10.48550\/arXiv.2205.04437","DOI":"10.48550\/arXiv.2205.04437"},{"key":"2277_CR35","doi-asserted-by":"publisher","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021). https:\/\/doi.org\/10.48550\/arXiv.2103.14030","DOI":"10.48550\/arXiv.2103.14030"},{"key":"2277_CR36","doi-asserted-by":"publisher","unstructured":"Zhang X, Zeng H, Guo S, Zhang L (2022) Efficient long-range attention network for image super-resolution. In: European Conference on Computer Vision, pp. 649\u2013667. Springer. https:\/\/doi.org\/10.48550\/arXiv.2203.06697","DOI":"10.48550\/arXiv.2203.06697"},{"key":"2277_CR37","doi-asserted-by":"publisher","unstructured":"Choi H, Lee J, Yang J(2023) N-gram in swin transformers for efficient lightweight image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2071\u20132081 . https:\/\/doi.org\/10.48550\/arXiv.2211.11436","DOI":"10.48550\/arXiv.2211.11436"},{"key":"2277_CR38","doi-asserted-by":"publisher","unstructured":"Cai H, Gan C, Han S (2022) Efficientvit: Enhanced linear attention for high-resolution low-computation visual recognition. arXiv preprint arXiv:2205.14756 . https:\/\/doi.org\/10.48550\/arXiv.2205.14756","DOI":"10.48550\/arXiv.2205.14756"},{"key":"2277_CR39","doi-asserted-by":"publisher","unstructured":"Choromanski K, Likhosherstov V, Dohan D, Song X, Gane A, Sarlos T, Hawkins P, Davis J, Mohiuddin A, Kaiser L, et al. (2020) Rethinking attention with performers. arXiv preprint arXiv:2009.14794. https:\/\/doi.org\/10.48550\/arXiv.2009.14794","DOI":"10.48550\/arXiv.2009.14794"},{"key":"2277_CR40","doi-asserted-by":"publisher","unstructured":"You, H., Xiong, Y., Dai, X., Wu, B., Zhang, P., Fan, H., Vajda, P., Lin, Y.C.: Castling-vit: Compressing self-attention via switching towards linear-angular attention at vision transformer inference. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14431\u201314442 (2023). https:\/\/doi.org\/10.48550\/arXiv.2211.10526","DOI":"10.48550\/arXiv.2211.10526"},{"key":"2277_CR41","doi-asserted-by":"publisher","unstructured":"Chen, B., Dao, T., Winsor, E., Song, Z., Rudra, A., R\u00e9, C.: Scatterbrain: Unifying sparse and low-rank attention. Advances in Neural Information Processing Systems 34, 17413\u201317426 (2021). https:\/\/doi.org\/10.48550\/arXiv.2110.15343","DOI":"10.48550\/arXiv.2110.15343"},{"key":"2277_CR42","doi-asserted-by":"publisher","unstructured":"Dass, J., Wu, S., Shi, H., Li, C., Ye, Z., Wang, Z., Lin, Y.: Vitality: Unifying low-rank and sparse approximation for vision transformer acceleration with a linear taylor attention. In: 2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA), pp. 415\u2013428 (2023). IEEE. https:\/\/doi.org\/10.48550\/arXiv.2211.05109","DOI":"10.48550\/arXiv.2211.05109"},{"key":"2277_CR43","doi-asserted-by":"publisher","unstructured":"Lipson L, Teed Z, Deng J (2021) Raft-stereo: Multilevel recurrent field transforms for stereo matching. In: 2021 International Conference on 3D Vision (3DV), pp. 218\u2013227. IEEE. https:\/\/doi.org\/10.48550\/arXiv.2109.07547","DOI":"10.48550\/arXiv.2109.07547"},{"key":"2277_CR44","doi-asserted-by":"publisher","unstructured":"Niu B, Wen W, Ren W, Zhang X, Yang L, Wang S, Zhang K, Cao X, Shen H (2020) Single image super-resolution via a holistic attention network. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XII 16, pp. 191\u2013207. Springer. https:\/\/doi.org\/10.48550\/arXiv.2008.08767","DOI":"10.48550\/arXiv.2008.08767"},{"key":"2277_CR45","doi-asserted-by":"publisher","unstructured":"Han D, Pan X, Han Y, Song S, Huang G (2023) Flatten transformer: Vision transformer using focused linear attention. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5961\u20135971. https:\/\/doi.org\/10.48550\/arXiv.2308.00442","DOI":"10.48550\/arXiv.2308.00442"},{"key":"2277_CR46","doi-asserted-by":"publisher","unstructured":"Zhang Y, Li K, Li K, Wang L, Zhong B, Fu Y (2018) Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 286\u2013301. https:\/\/doi.org\/10.48550\/arXiv.1807.02758","DOI":"10.48550\/arXiv.1807.02758"},{"key":"2277_CR47","doi-asserted-by":"publisher","unstructured":"Hui Z, Wang X, Gao X (2018) Fast and accurate single image super-resolution via information distillation network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 723\u2013731 . https:\/\/doi.org\/10.48550\/arXiv.1803.09454","DOI":"10.48550\/arXiv.1803.09454"},{"issue":"3","key":"2277_CR48","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1109\/TCYB.2020.2970104","volume":"51","author":"R Lan","year":"2020","unstructured":"Lan R, Sun L, Liu Z, Lu H, Pang C, Luo X (2020) Madnet: A fast and lightweight network for single-image super resolution. IEEE Trans Cybernet 51(3):1443\u20131453. https:\/\/doi.org\/10.1109\/TCYB.2020.2970104","journal-title":"IEEE Trans Cybernet"},{"key":"2277_CR49","doi-asserted-by":"publisher","unstructured":"Wang L, Dong X, Wang Y, Ying X, Lin Z, An W, Guo Y (2021) Exploring sparsity in image super-resolution for efficient inference. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4917\u20134926. https:\/\/doi.org\/10.48550\/arXiv.2006.09603","DOI":"10.48550\/arXiv.2006.09603"},{"key":"2277_CR50","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3134172","author":"K Park","year":"2021","unstructured":"Park K, Soh JW, Cho NI (2021) Dynamic residual self-attention network for lightweight single image super-resolution. IEEE Trans Multimedia. https:\/\/doi.org\/10.1109\/TMM.2021.3134172","journal-title":"IEEE Trans Multimedia"},{"key":"2277_CR51","doi-asserted-by":"publisher","unstructured":"Luo X, Qu Y, Xie Y, Zhang Y, Li C, Fu Y (2022) Lattice network for lightweight image restoration. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(4), 4826\u20134842. https:\/\/doi.org\/10.48550\/arXiv.2112.08655","DOI":"10.48550\/arXiv.2112.08655"},{"key":"2277_CR52","doi-asserted-by":"publisher","unstructured":"Sun B, Zhang Y, Jiang S, Fu Y (2023) Hybrid pixel-unshuffled network for lightweight image super-resolution. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 2375\u20132383. https:\/\/doi.org\/10.48550\/arXiv.2203.08921","DOI":"10.48550\/arXiv.2203.08921"},{"key":"2277_CR53","doi-asserted-by":"publisher","unstructured":"Timofte, R, Agustsson E, Van\u00a0Gool L, Yang M-H, Zhang L (2017) Ntire 2017 challenge on single image super-resolution: Methods and results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 114\u2013125. https:\/\/doi.org\/10.1109\/CVPRW.2017.149","DOI":"10.1109\/CVPRW.2017.149"},{"key":"2277_CR54","doi-asserted-by":"publisher","DOI":"10.5244\/C.26.135","author":"M Bevilacqua","year":"2012","unstructured":"Bevilacqua M, Roumy A, Guillemot C, Alberi-Morel ML (2012). Low-complexity single-image super-resolution based on nonnegative neighbor embedding. https:\/\/doi.org\/10.5244\/C.26.135","journal-title":"Low-complexity single-image super-resolution based on nonnegative neighbor embedding"},{"key":"2277_CR55","doi-asserted-by":"publisher","unstructured":"Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Curves and Surfaces: 7th International Conference, Avignon, France, June 24-30, 2010, Revised Selected Papers 7, pp. 711\u2013730 (2012). Springer. https:\/\/doi.org\/10.1007\/978-3-642-27413-8_47","DOI":"10.1007\/978-3-642-27413-8_47"},{"key":"2277_CR56","doi-asserted-by":"publisher","unstructured":"Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, vol. 2, pp. 416\u2013423 (2001). IEEE. https:\/\/doi.org\/10.1109\/ICCV.2001.937655","DOI":"10.1109\/ICCV.2001.937655"},{"key":"2277_CR57","doi-asserted-by":"publisher","unstructured":"Huang J-B, Singh A, Ahuja N (2015) Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5197\u20135206. https:\/\/doi.org\/10.1109\/CVPR.2015.7299156","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"2277_CR58","doi-asserted-by":"publisher","unstructured":"Matsui Y, Ito K, Aramaki Y, Fujimoto A, Ogawa T, Yamasaki T, Aizawa K (2017) Sketch-based manga retrieval using manga109 dataset. Multimedia Tools and Applications 76, 21811\u201321838. https:\/\/doi.org\/10.48550\/arXiv.1510.04389","DOI":"10.48550\/arXiv.1510.04389"},{"key":"2277_CR59","doi-asserted-by":"publisher","unstructured":"Agustsson E, Timofte R (2017) Ntire 2017 challenge on single image super-resolution: Dataset and study. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 126\u2013135. https:\/\/doi.org\/10.1109\/CVPRW.2017.150","DOI":"10.1109\/CVPRW.2017.150"},{"key":"2277_CR60","doi-asserted-by":"publisher","unstructured":"Cai J, Zeng H, Yong H, Cao Z, Zhang L (2019) Toward real-world single image super-resolution: A new benchmark and a new model. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3086\u20133095. https:\/\/doi.org\/10.48550\/arXiv.1904.00523","DOI":"10.48550\/arXiv.1904.00523"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02277-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02277-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02277-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T10:24:52Z","timestamp":1730197492000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02277-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,9]]},"references-count":60,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["2277"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02277-2","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2024,7,9]]},"assertion":[{"value":"27 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}