{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:25:37Z","timestamp":1760145937235,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T00:00:00Z","timestamp":1727136000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["52204177","52304182","SCIP20240105","MSSB-2024-01"],"award-info":[{"award-number":["52204177","52304182","SCIP20240105","MSSB-2024-01"]}]},{"name":"Open Fund of Key Laboratory of System Control and Information Processing of the Ministry of Education of China","award":["52204177","52304182","SCIP20240105","MSSB-2024-01"],"award-info":[{"award-number":["52204177","52304182","SCIP20240105","MSSB-2024-01"]}]},{"name":"Key Laboratory of Pattern Recognition and Intelligent Information Processing, Institutions of Higher Education of Sichuan Province","award":["52204177","52304182","SCIP20240105","MSSB-2024-01"],"award-info":[{"award-number":["52204177","52304182","SCIP20240105","MSSB-2024-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To enhance the performance of super-resolution models, neural networks frequently employ module stacking. However, this approach inevitably results in an excessive proliferation of parameter counts and information redundancy, ultimately constraining the deployment of these models on mobile devices. To surmount this limitation, this study introduces the application of Dual-path Large Kernel Learning (DLKL) to the task of image super-resolution. Within the DLKL framework, we harness a multiscale large kernel decomposition technique to efficiently establish long-range dependencies among pixels. This network not only maintains excellent performance but also significantly mitigates the parameter burden, achieving an optimal balance between network performance and efficiency. When compared with other prevalent algorithms, DLKL exhibits remarkable proficiency in generating images with sharper textures and structures that are more akin to natural ones. It is particularly noteworthy that on the challenging texture dataset Urban100, the network proposed in this study achieved a significant improvement in Peak Signal-to-Noise Ratio (PSNR) for the \u00d74 upscaling task, with an increase of 0.32 dB and 0.19 dB compared with the state-of-the-art HAFRN and MICU networks, respectively. This remarkable result not only validates the effectiveness of the present model in complex image super-resolution tasks but also highlights its superior performance and unique advantages in the field.<\/jats:p>","DOI":"10.3390\/s24196174","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T08:56:06Z","timestamp":1727168166000},"page":"6174","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual-Path Large Kernel Learning and Its Applications in Single-Image Super-Resolution"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2393-3818","authenticated-orcid":false,"given":"Zhen","family":"Su","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0197-5296","authenticated-orcid":false,"given":"Mang","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3345-9665","authenticated-orcid":false,"given":"He","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai 200240, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9310-5257","authenticated-orcid":false,"given":"Xiang","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2079-9417","authenticated-orcid":false,"given":"Chen","family":"Lv","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2873-2636","authenticated-orcid":false,"given":"Qiqi","family":"Kou","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Mining and Technology, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4475-845X","authenticated-orcid":false,"given":"Deqiang","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102398","DOI":"10.1016\/j.aei.2024.102398","article-title":"Indicative Vision Transformer for end-to-end zero-shot sketch-based image retrieval","volume":"60","author":"Zhang","year":"2024","journal-title":"Adv. Eng. Inform."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"57811","DOI":"10.1007\/s11042-023-17675-x","article-title":"Task-like training paradigm in CLIP for zero-shot sketch-based image retrieval","volume":"83","author":"Zhang","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"36329","DOI":"10.1007\/s11042-023-16914-5","article-title":"Single image detail enhancement via metropolis theorem","volume":"83","author":"Jiang","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"123798","DOI":"10.1109\/ACCESS.2023.3321976","article-title":"Dual-stage Super-resolution for edge devices","volume":"11","author":"Park","year":"2023","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"29062","DOI":"10.1109\/ACCESS.2023.3260159","article-title":"F2SRGAN: A Lightweight Approach Boosting Perceptual Quality in Single Image Super-Resolution via a Revised Fast Fourier Convolution","volume":"11","author":"Nguyen","year":"2023","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"76905","DOI":"10.1007\/s11042-024-18471-x","article-title":"Intermediate-term memory mechanism inspired lightweight single image super resolution","volume":"83","author":"Cheng","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_7","first-page":"1","article-title":"Image Super-Resolution Algorithms Based on Deep Feature Differentiation Network","volume":"46","author":"Cheng","year":"2024","journal-title":"J. Electron. Inf. Technol."},{"key":"ref_8","first-page":"73","article-title":"Lightweight Super-resolution Reconstruction Method Based on Hierarchical Features Fusion and Attention Mechanism for Mine Image","volume":"43","author":"Cheng","year":"2022","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_9","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":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., and Lee, K.M. (2015, January 7\u201312). Accurate Image Super-Resolution Using Very Deep Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2016.182"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Mu Lee, K. (2017, January 21\u201326). Enhanced Deep Residual Networks for Single Image Super-Resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honoluu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., and Lee, K.M. (2016, January 27\u201330). Deeply-Recursive Convolutional Network for Image Super-Resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.181"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., and Fu, Y. (2018, January 8\u201314). Image Super-Resolution Using Very Deep Residual Channel Attention Networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhao, H., Kong, X., He, J., Qiao, Y., and Dong, C. (2020, January 23\u201328). Efficient Image Super-Resolution Using Pixel Attention. Proceedings of the Computer Vision\u2014ECCV 2020 Workshops, Glasgow, UK.","DOI":"10.1007\/978-3-030-67070-2_3"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., and Gao, W. (2020, January 13\u201319). Pre-Trained Image Processing Transformer. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR46437.2021.01212"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"98653","DOI":"10.1109\/ACCESS.2023.3314196","article-title":"Single Image Super Resolution via Multi-Attention Fusion Recurrent Network","volume":"11","author":"Kou","year":"2023","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1109\/TBC.2020.3028356","article-title":"Lightweight Image Super-Resolution by Multi-Scale Aggregation","volume":"67","author":"Wan","year":"2021","journal-title":"IEEE Trans. Broadcast."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, Z., Gao, G., Li, J., Yu, Y., and Lu, H. (2021, January 5\u20139). Lightweight Image Super-Resolution with Multi-Scale Feature Interaction Network. Proceedings of the 2021 IEEE International Conference on Multimedia and Expo (ICME), Shenzhen, China.","DOI":"10.1109\/ICME51207.2021.9428136"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1109\/TCYB.2020.2970104","article-title":"MADNet: A Fast and Lightweight Network for Single-Image Super Resolution","volume":"51","author":"Lan","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1007\/s41095-023-0364-2","article-title":"Visual attention network","volume":"9","author":"Guo","year":"2022","journal-title":"Comput. Vis. Media"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2019, January 15\u201320). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bevilacqua, M., Roumy, A., Guillemot, C., and Alberi-Morel, M.L. (2012, January 3\u20137). Low-complexity single-image super-resolution based on nonnegative neighbor embedding. Proceedings of the British Machine Vision Conference, Surrey, UK.","DOI":"10.5244\/C.26.135"},{"key":"ref_24","unstructured":"Zeyde, R., Elad, M., and Protter, M. (2010). On Single Image Scale-Up Using Sparse-Representations. Curves and Surfaces, Springer."},{"key":"ref_25","unstructured":"Martin, D., Fowlkes, C., Tal, D., and Malik, J. (2001, January 7\u201314). A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. Proceedings of the Eighth IEEE International Conference on Computer Vision, Vancouver, BC, Canada."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Huang, J.B., Singh, A., and Ahuja, N. (2015, January 7\u201312). Single image super-resolution from transformed self-exemplars. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lai, W.S., Huang, J.B., Ahuja, N., and Yang, M.H. (2017, January 21\u201326). Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.618"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hui, Z., Gao, X., Yang, Y., and Wang, X. (2019, January 21\u201325). Lightweight Image Super-Resolution with Information Multi-distillation Network. Proceedings of the ACM International Conference on Multimedia, Nice, France.","DOI":"10.1145\/3343031.3351084"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, L., Dong, X., Wang, Y., Ying, X., Lin, Z., An, W., and Guo, Y. (2021, January 20\u201325). Exploring Sparsity in Image Super-Resolution for Efficient Inference. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00488"},{"key":"ref_30","first-page":"17314","article-title":"Shufflemixer: An efficient convnet for image super-resolution","volume":"35","author":"Sun","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Gao, G., Wang, Z., Li, J., Li, W., Yu, Y., and Zeng, T. (2022, January 23\u201329). Lightweight bimodal network for single-image super-resolution via symmetric CNN and recursive transformer. Proceedings of the International Joint Conferences on Artificial Intelligence Organization, Vienna, Austria.","DOI":"10.24963\/ijcai.2022\/128"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1016\/j.neucom.2022.05.066","article-title":"Lightweight single image super-resolution with attentive residual refinement network","volume":"500","author":"Qin","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_33","first-page":"4826","article-title":"Lattice Network for Lightweight Image Restoration","volume":"45","author":"Luo","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2183","DOI":"10.1109\/TII.2022.3190350","article-title":"Progressive interaction-learning network for lightweight single-image super-resolution in industrial applications","volume":"19","author":"Qin","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2672","DOI":"10.1109\/TCSVT.2022.3230824","article-title":"Differentiable Neural Architecture Search for Extremely Lightweight Image Super-Resolution","volume":"33","author":"Huang","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"123111","DOI":"10.1016\/j.eswa.2023.123111","article-title":"MICU: Image super-resolution via multi-level information compensation and U-net","volume":"245","author":"Chen","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3232","DOI":"10.1109\/TCE.2023.3329813","article-title":"Hybrid attention feature refinement network for lightweight image super-resolution in metaverse immersive display","volume":"70","author":"Wanga","year":"2024","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"123954","DOI":"10.1016\/j.eswa.2024.123954","article-title":"Lightweight blueprint residual network for single image super-resolution","volume":"250","author":"Hao","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"52202","DOI":"10.1109\/ACCESS.2021.3069775","article-title":"Lightweight Attended Multi-Scale Residual Network for Single Image Super-Resolution","volume":"9","author":"Yan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lu, Z., Li, J., Liu, H., Huang, C., Zhang, L., and Zeng, T. (2022, January 18\u201324). Transformer for single image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00061"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Choi, H., Lee, J., and Yang, J. (2023, January 17\u201324). N-gram in swin transformers for efficient lightweight image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00206"},{"key":"ref_42","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_43","first-page":"2810","article-title":"Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections","volume":"29","author":"Mao","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., Liu, X., and Xu, C. (2017, January 21\u201326). Memnet: A persistent memory network for image restoration. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/ICCV.2017.486"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Change Loy, C., Qiao, Y., and Tang, X. (2018, January 8\u201314). Esrgan: Enhanced super-resolution generative adversarial networks. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Munich, Germany.","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Muqeet, A., Hwang, J., Yang, S., Kang, J., Kim, Y., and Bae, S.H. (2020, January 23\u201328). Multi-attention based ultra lightweight image super-resolution. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Glasgow, UK.","DOI":"10.1007\/978-3-030-67070-2_6"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6174\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:01:39Z","timestamp":1760112099000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6174"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,24]]},"references-count":47,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["s24196174"],"URL":"https:\/\/doi.org\/10.3390\/s24196174","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,9,24]]}}}