{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T20:10:18Z","timestamp":1772050218313,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T00:00:00Z","timestamp":1680566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF)","doi-asserted-by":"publisher","award":["2022R1A2C200289711"],"award-info":[{"award-number":["2022R1A2C200289711"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Various super-resolution (SR) kernels in the degradation model deteriorate the performance of the SR algorithms, showing unpleasant artifacts in the output images. Hence, SR kernel estimation has been studied to improve the SR performance in several ways for more than a decade. In particular, a conventional research named KernelGAN has recently been proposed. To estimate the SR kernel from a single image, KernelGAN introduces generative adversarial networks(GANs) that utilize the recurrence of similar structures across scales. Subsequently, an enhanced version of KernelGAN, named E-KernelGAN, was proposed to consider image sharpness and edge thickness. Although it is stable compared to the earlier method, it still encounters challenges in estimating sizable and anisotropic kernels because the structural information of an input image is not sufficiently considered. In this paper, we propose a kernel estimation algorithm called Total Variation Guided KernelGAN (TVG-KernelGAN), which efficiently enables networks to focus on the structural information of an input image. The experimental results show that the proposed algorithm accurately and stably estimates kernels, particularly sizable and anisotropic kernels, both qualitatively and quantitatively. In addition, we compared the results of the non-blind SR methods, using SR kernel estimation techniques. The results indicate that the performance of the SR algorithms was improved using our proposed method.<\/jats:p>","DOI":"10.3390\/s23073734","type":"journal-article","created":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T02:11:41Z","timestamp":1680660701000},"page":"3734","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Kernel Estimation Using Total Variation Guided GAN for Image Super-Resolution"],"prefix":"10.3390","volume":"23","author":[{"given":"Jongeun","family":"Park","sequence":"first","affiliation":[{"name":"School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hansol","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moon Gi","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/MSP.2003.1203207","article-title":"Super-resolution image reconstruction: A technical overview","volume":"20","author":"Park","year":"2003","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1109\/TIP.2004.834669","article-title":"Fast and robust multiframe super resolution","volume":"13","author":"Farsiu","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1117\/1.601623","article-title":"High-resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system","volume":"37","author":"Hardie","year":"1998","journal-title":"Opt. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1109\/TCSVT.2011.2163447","article-title":"Multiframe super-resolution employing a spatially weighted total variation model","volume":"22","author":"Yuan","year":"2011","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/TCI.2016.2516909","article-title":"Robust multiframe super-resolution employing iteratively re-weighted minimization","volume":"2","author":"Huang","year":"2016","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Glasner, D., Bagon, S., and Irani, M. (October, January 29). Super-resolution from a single image. Proceedings of the 2009 IEEE 12th International Conference on Computer Vision, Kyoto, Japan.","DOI":"10.1109\/ICCV.2009.5459271"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Michaeli, T., and Irani, M. (2013, January 13). Nonparametric Blind Super-resolution. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Nice, France.","DOI":"10.1109\/ICCV.2013.121"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/38.988747","article-title":"Example-based super-resolution","volume":"22","author":"Freeman","year":"2002","journal-title":"IEEE Comput. Graph. Appl."},{"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":"2015","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. (2016, January 27\u201330). Accurate image super-resolution using very deep convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NA, 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, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Haris, M., Shakhnarovich, G., and Ukita, N. (2018, January 18\u201322). Deep back-projection networks for super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00179"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shocher, A., Cohen, N., and Irani, M. (2018, January 18\u201322). \u201czero-shot\u201d super-resolution using deep internal learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00329"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., and Wang, Z. (2017, January 21\u201326). Photo-realistic single image super-resolution using a generative adversarial network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ma, C., Rao, Y., Cheng, Y., Chen, C., Lu, J., and Zhou, J. (2020, January 13\u201319). Structure-preserving super resolution with gradient guidance. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00779"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ji, X., Cao, Y., Tai, Y., Wang, C., Li, J., and Huang, F. (2020, January 14\u201319). Real-world super-resolution via kernel estimation and noise injection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00241"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lugmayr, A., Danelljan, M., and Timofte, R. (2019, January 27\u201328). Unsupervised learning for real-world super-resolution. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Republic of Korea.","DOI":"10.1109\/ICCVW.2019.00423"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lugmayr, A., Danelljan, M., Timofte, R., Fritsche, M., Gu, S., Purohit, K., Kandula, P., Suin, M., Rajagoapalan, A., and Joon, N.H. (2019, January 27\u201328). Aim 2019 challenge on real-world image super-resolution: Methods and results. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Republic of Korea.","DOI":"10.1109\/ICCVW.2019.00442"},{"key":"ref_19","unstructured":"Lugmayr, A., Danelljan, M., and Timofte, R. (2020, January 19\u201325). Ntire 2020 challenge on real-world image super-resolution: Methods and results. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Nashville, TN, USA."},{"key":"ref_20","unstructured":"Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., and Garnett, R. (2019). Proceedings of the Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liang, J., Zhang, K., Gu, S., Van Gool, L., and Timofte, R. (2021, January 20\u201325). Flow-based kernel prior with application to blind super-resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01046"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"45179","DOI":"10.1109\/ACCESS.2022.3170053","article-title":"Unsupervised Blur Kernel Estimation and Correction for Blind Super-Resolution","volume":"10","author":"Kim","year":"2022","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yamac, M., Ataman, B., and Nawaz, A. (2021, January 19\u201325). KernelNet: A Blind Super-Resolution Kernel Estimation Network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Nashville, TN, USA.","DOI":"10.1109\/CVPRW53098.2021.00056"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Cho, S., and Lee, S. (2009, January 16\u201319). Fast Motion Deblurring. Proceedings of the ACM SIGGRAPH Asia 2009 Papers, SIGGRAPH Asia \u201909, Yokohama, Japan.","DOI":"10.1145\/1661412.1618491"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1409060.1409106","article-title":"High-Quality Motion Deblurring from a Single Image","volume":"27","author":"Shan","year":"2008","journal-title":"ACM Trans. Graph."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Agustsson, E., and Timofte, R. (2017, January 21\u201326). NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.150"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Timofte, R., Agustsson, E., Van Gool, L., Yang, M.H., and Zhang, L. (2017, January 21\u201326). NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results. Proceedings of the The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.150"},{"key":"ref_28","unstructured":"Ulyanov, D., Vedaldi, A., and Lempitsky, V. (2018, January 18\u201323). Deep image prior. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., and Schmid, C. (2012, January 7\u201313). Good Regions to Deblur. Proceedings of the Computer Vision\u2014ECCV 2012, Florence, Italy.","DOI":"10.1007\/978-3-642-33709-3"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/7\/3734\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:09:55Z","timestamp":1760123395000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/7\/3734"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,4]]},"references-count":29,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["s23073734"],"URL":"https:\/\/doi.org\/10.3390\/s23073734","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,4]]}}}