{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T22:05:46Z","timestamp":1782857146484,"version":"3.54.5"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T00:00:00Z","timestamp":1670803200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T00:00:00Z","timestamp":1670803200000},"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":["SIViP"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s11760-022-02421-x","type":"journal-article","created":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T17:05:58Z","timestamp":1670864758000},"page":"2073-2081","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Thermal image super-resolution via multi-path residual attention network"],"prefix":"10.1007","volume":"17","author":[{"given":"Haikun","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yueli","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,12,12]]},"reference":[{"key":"2421_CR1","doi-asserted-by":"crossref","unstructured":"Chudasama, V., Patel, H., Prajapati, K., Upla, K.P., Ramachandra, R., Raja, K., Busch, C.: Therisurnet-a computationally efficient thermal image super-resolution network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 86\u201387 (2020)","DOI":"10.1109\/CVPRW50498.2020.00051"},{"key":"2421_CR2","doi-asserted-by":"publisher","first-page":"52508","DOI":"10.1109\/ACCESS.2022.3175317","volume":"10","author":"M Rostami","year":"2022","unstructured":"Rostami, M., Oussalah, M., Farrahi, V.: A novel time-aware food recommender-system based on deep learning and graph clustering. IEEE Access 10, 52508\u201352524 (2022)","journal-title":"IEEE Access"},{"key":"2421_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105766","volume":"147","author":"S Azadifar","year":"2022","unstructured":"Azadifar, S., Rostami, M., Berahmand, K., Moradi, P., Oussalah, M.: Graph-based relevancy-redundancy gene selection method for cancer diagnosis. Comput. Biol. Med. 147, 105766 (2022)","journal-title":"Comput. Biol. Med."},{"issue":"10","key":"2421_CR4","doi-asserted-by":"publisher","first-page":"3365","DOI":"10.1109\/TPAMI.2020.2982166","volume":"43","author":"Z Wang","year":"2020","unstructured":"Wang, Z., Chen, J., Hoi, S.C.: Deep learning for image super-resolution: a survey. IEEE Trans. Pattern Anal. 43(10), 3365\u20133387 (2020)","journal-title":"IEEE Trans. Pattern Anal."},{"key":"2421_CR5","doi-asserted-by":"crossref","unstructured":"Choi, Y., Kim, N., Hwang, S., Kweon, I.S.: Thermal image enhancement using convolutional neural network. In: 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 223\u2013230. IEEE (2016)","DOI":"10.1109\/IROS.2016.7759059"},{"key":"2421_CR6","doi-asserted-by":"crossref","unstructured":"Rivadeneira, R.E., Su\u00e1rez, P.L., Sappa, A.D., Vintimilla, B.X.: Thermal image superresolution through deep convolutional neural network. In: International Conference on Image Analysis and Recognition, pp. 417\u2013426. Springer (2019)","DOI":"10.1007\/978-3-030-27272-2_37"},{"key":"2421_CR7","doi-asserted-by":"crossref","unstructured":"Zhou, L., Cai, H., Gu, J., Li, Z., Liu, Y., Chen, X., Qiao, Y., Dong, C.: Efficient image super-resolution using vast-receptive-field attention. arXiv preprint arXiv:2210.05960 (2022)","DOI":"10.1007\/978-3-031-25063-7_16"},{"key":"2421_CR8","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251\u20131258 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"2421_CR9","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"2421_CR10","doi-asserted-by":"crossref","unstructured":"Timofte, R., Rothe, R., Van\u00a0Gool, L.: Seven ways to improve example-based single image super resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1865\u20131873 (2016)","DOI":"10.1109\/CVPR.2016.206"},{"key":"2421_CR11","doi-asserted-by":"crossref","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. arXiv:1710.09412 (2017)","DOI":"10.1007\/978-1-4899-7687-1_79"},{"key":"2421_CR12","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: Regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6023\u20136032 (2019)","DOI":"10.1109\/ICCV.2019.00612"},{"key":"2421_CR13","doi-asserted-by":"crossref","unstructured":"Yoo, J., Ahn, N., Sohn, K.-A.: Rethinking data augmentation for image super-resolution: a comprehensive analysis and a new strategy. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8375\u20138384 (2020)","DOI":"10.1109\/CVPR42600.2020.00840"},{"key":"2421_CR14","unstructured":"DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv:1708.04552 (2017)"},{"key":"2421_CR15","unstructured":"Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Lopez-Paz, D., Bengio, Y.: Manifold mixup: better representations by interpolating hidden states. In: International Conference on Machine Learning, pp. 6438\u20136447. PMLR (2019)"},{"key":"2421_CR16","unstructured":"Gastaldi, X.: Shake-shake regularization. arXiv:1705.07485 (2017)"},{"key":"2421_CR17","doi-asserted-by":"publisher","first-page":"186126","DOI":"10.1109\/ACCESS.2019.2960566","volume":"7","author":"Y Yamada","year":"2019","unstructured":"Yamada, Y., Iwamura, M., Akiba, T., Kise, K.: Shakedrop regularization for deep residual learning. IEEE Access 7, 186126\u2013186136 (2019)","journal-title":"IEEE Access"},{"issue":"1","key":"2421_CR18","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"2421_CR19","unstructured":"Ghiasi, G., Lin, T.-Y., Le, Q.V.: Dropblock: a regularization method for convolutional networks. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"issue":"12","key":"2421_CR20","doi-asserted-by":"publisher","first-page":"4256","DOI":"10.1109\/TPAMI.2020.2999099","volume":"43","author":"J Choe","year":"2020","unstructured":"Choe, J., Lee, S., Shim, H.: Attention-based dropout layer for weakly supervised single object localization and semantic segmentation. IEEE Trans. Pattern Anal. 43(12), 4256\u20134271 (2020)","journal-title":"IEEE Trans. Pattern Anal."},{"issue":"11","key":"2421_CR21","doi-asserted-by":"publisher","first-page":"2861","DOI":"10.1109\/TIP.2010.2050625","volume":"19","author":"J Yang","year":"2010","unstructured":"Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE. Trans. Image Process. 19(11), 2861\u20132873 (2010)","journal-title":"IEEE. Trans. Image Process."},{"issue":"2","key":"2421_CR22","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2015","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE. Trans. Pattern Anal. 38(2), 295\u2013307 (2015)","journal-title":"IEEE. Trans. Pattern Anal."},{"key":"2421_CR23","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1646\u20131654 (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"2421_CR24","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: Enhanced deep residual networks for single image super-resolution. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1132\u20131140 (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"2421_CR25","doi-asserted-by":"crossref","unstructured":"Li, J., Fang, F., Mei, K., Zhang, G.: Multi-scale residual network for image super-resolution. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 517\u2013532 (2018)","DOI":"10.1007\/978-3-030-01237-3_32"},{"key":"2421_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 286\u2013301 (2018)","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"2421_CR27","doi-asserted-by":"crossref","unstructured":"Rivadeneira, R.E., Sappa, A.D., Vintimilla, B.X., Guo, L., Hou, J., Mehri, A., Ardakani, P.B., Patel, H., Chudasama, V., Prajapati, K., Upla, K.P., Ramachandra, R., Raja, K., Busch, C., Almasri, F., Debeir, O., Nathan, S., Kansal, P., Gutierrez, N., Mojra, B., Beksi, W.J.: Thermal image super-resolution challenge\u2014PBVS 2020. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020)","DOI":"10.1109\/CVPRW50498.2020.00056"},{"key":"2421_CR28","doi-asserted-by":"crossref","unstructured":"Rivadeneira, R.E., Sappa, A.D., Vintimilla, B.X., Nathan, S., Kansal, P., Mehri, A., Ardakani, P.B., Dalal, A., Akula, A., Sharma, D., et al.: Thermal image super-resolution challenge-pbvs 2021. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4359\u20134367 (2021)","DOI":"10.1109\/CVPRW53098.2021.00492"},{"key":"2421_CR29","doi-asserted-by":"crossref","unstructured":"Prajapati, K., Chudasama, V., Patel, H., Sarvaiya, A., Upla, K.P., Raja, K., Ramachandra, R., Busch, C.: Channel split convolutional neural network (CHASNET) for thermal image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4368\u20134377 (2021)","DOI":"10.1109\/CVPRW53098.2021.00493"},{"key":"2421_CR30","unstructured":"Clevert, D.-A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (elus). arXiv:1511.07289 (2015)"},{"key":"2421_CR31","doi-asserted-by":"crossref","unstructured":"Fu, B., Dong, Y., Fu, S., Wu, Y., Ren, Y., Thanh, D.: Multistage supervised contrastive learning for hybrid-degraded image restoration. Signal Image Video, pp. 1\u20139 (2022)","DOI":"10.1007\/s11760-022-02262-8"},{"key":"2421_CR32","doi-asserted-by":"crossref","unstructured":"Chen, X., Yang, R., Guo, C.: A lightweight multi-scale residual network for single image super-resolution. Signal Image Video, pp. 1\u20139 (2022)","DOI":"10.1109\/ACCESS.2021.3069775"},{"key":"2421_CR33","doi-asserted-by":"crossref","unstructured":"Rivadeneira, R., Sappa, A., Vintimilla, B.: Thermal image super-resolution: a novel architecture and dataset. In: 15th International Conference on Computer Vision Theory and Applications (2020)","DOI":"10.5220\/0009173601110119"},{"key":"2421_CR34","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: European Conference on Computer Vision, pp. 391\u2013407. Springer (2016)","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"2421_CR35","doi-asserted-by":"crossref","unstructured":"Jo, Y., Kim, S.J.: Practical single-image super-resolution using look-up table. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 691\u2013700 (2021)","DOI":"10.1109\/CVPR46437.2021.00075"},{"key":"2421_CR36","doi-asserted-by":"crossref","unstructured":"Martini, M.G., Hewage, C.T., Villarini, B.: Image quality assessment based on edge preservation. Signal Process. Image Commun. 27(8), 875\u2013882 (2012)","DOI":"10.1016\/j.image.2012.01.012"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-022-02421-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-022-02421-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-022-02421-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T04:15:58Z","timestamp":1684383358000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-022-02421-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,12]]},"references-count":36,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["2421"],"URL":"https:\/\/doi.org\/10.1007\/s11760-022-02421-x","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,12]]},"assertion":[{"value":"16 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}