{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T23:24:21Z","timestamp":1761953061123,"version":"build-2065373602"},"publisher-location":"Singapore","reference-count":41,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819533978","type":"print"},{"value":"9789819533985","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-3398-5_40","type":"book-chapter","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T23:19:57Z","timestamp":1761952797000},"page":"493-506","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging a\u00a0Dual-Learning Methodology Based on\u00a0Degradation Modeling and\u00a0Fractional Fourier Image Transformer for\u00a0Light Field Image Super-Resolution"],"prefix":"10.1007","author":[{"given":"Haiyang","family":"Liu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4448-592X","authenticated-orcid":false,"given":"Jian","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Sheng","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Dong","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Linsheng","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,1]]},"reference":[{"key":"40_CR1","unstructured":"Ng, R., Levoy, M., Br\u00e9dif, M., Duval, G., Horowitz, M., Hanrahan, P.: Light field photography with a hand-held plenoptic camera. Ph.D. thesis, Stanford university (2005)"},{"issue":"3","key":"40_CR2","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1109\/TPAMI.2013.147","volume":"36","author":"S Wanner","year":"2013","unstructured":"Wanner, S., Goldluecke, B.: Variational light field analysis for disparity estimation and super-resolution. IEEE Trans. Pattern Anal. Mach. Intell. 36(3), 606\u2013619 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"40_CR3","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wu, T., Yang, J., Wang, L., An, W., Guo, Y.: DeOccNet: learning to see through foreground occlusions in light fields. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 118\u2013127 (2020)","DOI":"10.1109\/WACV45572.2020.9093448"},{"issue":"11","key":"40_CR4","doi-asserted-by":"publisher","first-page":"2170","DOI":"10.1109\/TPAMI.2016.2515615","volume":"38","author":"T-C Wang","year":"2016","unstructured":"Wang, T.-C., Efros, A.A., Ramamoorthi, R.: Depth estimation with occlusion modeling using light-field cameras. IEEE Trans. Pattern Anal. Mach. Intell. 38(11), 2170\u20132181 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"40_CR5","doi-asserted-by":"crossref","unstructured":"Yoon, Y., Jeon, H.-G., Yoo, D., Lee, J.-Y.,\u00a0Kweon, I.S.: Learning a deep convolutional network for light-field image super-resolution. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 24\u201332 (2015)","DOI":"10.1109\/ICCVW.2015.17"},{"key":"40_CR6","doi-asserted-by":"publisher","first-page":"1057","DOI":"10.1109\/TIP.2020.3042059","volume":"30","author":"Y Wang","year":"2020","unstructured":"Wang, Y., et al.: Light field image super-resolution using deformable convolution. IEEE Trans. Image Process. 30, 1057\u20131071 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"40_CR7","doi-asserted-by":"publisher","first-page":"5956","DOI":"10.1109\/TIP.2021.3079805","volume":"30","author":"S Zhang","year":"2021","unstructured":"Zhang, S., Chang, S., Lin, Y.: End-to-end light field spatial super-resolution network using multiple epipolar geometry. IEEE Trans. Image Process. 30, 5956\u20135968 (2021)","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"40_CR8","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1109\/TPAMI.2022.3152488","volume":"45","author":"Y Wang","year":"2022","unstructured":"Wang, Y., et al.: Disentangling light fields for super-resolution and disparity estimation. IEEE Trans. Pattern Anal. Mach. Intell. 45(1), 425\u2013443 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"40_CR9","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1109\/LSP.2022.3146798","volume":"29","author":"Z Liang","year":"2022","unstructured":"Liang, Z., Wang, Y., Wang, L., Yang, J., Zhou, S.: Light field image super-resolution with transformers. IEEE Signal Process. Lett. 29, 563\u2013567 (2022)","journal-title":"IEEE Signal Process. Lett."},{"issue":"11","key":"40_CR10","doi-asserted-by":"publisher","first-page":"2886","DOI":"10.1364\/OL.522701","volume":"49","author":"J Ma","year":"2024","unstructured":"Ma, J., Li, Z., Cheng, J., An, P., Liang, D., Huang, L.: Light field image super-resolution based on dual learning and deep Fourier channel attention. Opt. Lett. 49(11), 2886\u20132889 (2024)","journal-title":"Opt. Lett."},{"issue":"2","key":"40_CR11","doi-asserted-by":"publisher","first-page":"2314","DOI":"10.1109\/TNNLS.2022.3189994","volume":"35","author":"X Zhao","year":"2024","unstructured":"Zhao, X.: Fractional Fourier image transformer for multimodal remote sensing data classification. IEEE Trans. Neural Netw. Learn. Syst. 35(2), 2314\u20132326 (2024)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"40_CR12","doi-asserted-by":"crossref","unstructured":"Wang, Y., Liang, Z., Wang, L., Yang, J., An, W., Guo, Y.: Real-world light field image super-resolution via degradation modulation. IEEE Trans. Neural Netw. Learn. Syst. (2024)","DOI":"10.1109\/TNNLS.2024.3378420"},{"key":"40_CR13","doi-asserted-by":"crossref","unstructured":"Yi, Z., Zhang, H., Tan, P., Gong, M.: DualGAN: unsupervised dual learning for image-to-image translation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2849\u20132857 (2017)","DOI":"10.1109\/ICCV.2017.310"},{"key":"40_CR14","doi-asserted-by":"crossref","unstructured":"Mitra, K., Veeraraghavan, A.: Light field denoising, light field superresolution and stereo camera based refocussing using a GMM light field patch prior. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 22\u201328. IEEE (2012)","DOI":"10.1109\/CVPRW.2012.6239346"},{"issue":"7","key":"40_CR15","doi-asserted-by":"publisher","first-page":"1058","DOI":"10.1109\/JSTSP.2017.2747127","volume":"11","author":"RA Farrugia","year":"2017","unstructured":"Farrugia, R.A., Galea, C., Guillemot, C.: Super resolution of light field images using linear subspace projection of patch-volumes. IEEE J. Sel. Top. Sig. Process. 11(7), 1058\u20131071 (2017)","journal-title":"IEEE J. Sel. Top. Sig. Process."},{"key":"40_CR16","doi-asserted-by":"crossref","unstructured":"Egiazarian, K., Katkovnik, V.: Single image super-resolution via BM3D sparse coding. In: 2015 23rd European Signal Processing Conference (EUSIPCO), pp. 2849\u20132853. IEEE (2015)","DOI":"10.1109\/EUSIPCO.2015.7362905"},{"key":"40_CR17","doi-asserted-by":"crossref","unstructured":"Alain, M., Smolic, A.: Light field denoising by sparse 5D transform domain collaborative filtering. In: 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP), pp. 1\u20136. IEEE (2017)","DOI":"10.1109\/MMSP.2017.8122232"},{"issue":"9","key":"40_CR18","doi-asserted-by":"publisher","first-page":"4207","DOI":"10.1109\/TIP.2018.2828983","volume":"27","author":"M Rossi","year":"2018","unstructured":"Rossi, M., Frossard, P.: Geometry-consistent light field super-resolution via graph-based regularization. IEEE Trans. Image Process. 27(9), 4207\u20134218 (2018)","journal-title":"IEEE Trans. Image Process."},{"issue":"9","key":"40_CR19","doi-asserted-by":"publisher","first-page":"1359","DOI":"10.1109\/LSP.2018.2856619","volume":"25","author":"Y Yuan","year":"2018","unstructured":"Yuan, Y., Cao, Z., Lijuan, S.: Light-field image superresolution using a combined deep CNN based on EPI. IEEE Signal Process. Lett. 25(9), 1359\u20131363 (2018)","journal-title":"IEEE Signal Process. Lett."},{"key":"40_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, S., Lin, Y., Sheng, H.: Residual networks for light field image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11046\u201311055 (2019)","DOI":"10.1109\/CVPR.2019.01130"},{"key":"40_CR21","doi-asserted-by":"crossref","unstructured":"Jin, J., Hou, J., Chen, J., Kwong, S.: Light field spatial super-resolution via deep combinatorial geometry embedding and structural consistency regularization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2260\u20132269 (2020)","DOI":"10.1109\/CVPR42600.2020.00233"},{"key":"40_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1007\/978-3-030-01234-2_18","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 294\u2013310. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_18"},{"key":"40_CR23","doi-asserted-by":"crossref","unstructured":"HeK, M.,\u00a0RenS, Q., et\u00a0al.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"40_CR24","doi-asserted-by":"crossref","unstructured":"Wu, G., Zhao, M., Wang, L., Dai, Q., Chai, T., Liu, Y.: Light field reconstruction using deep convolutional network on EPI. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6319\u20136327 (2017)","DOI":"10.1109\/CVPR.2017.178"},{"issue":"5","key":"40_CR25","doi-asserted-by":"publisher","first-page":"1338","DOI":"10.1109\/78.839981","volume":"48","author":"S-C Pei","year":"2000","unstructured":"Pei, S.-C., Ding, J.-J.: Closed-form discrete fractional and affine Fourier transforms. IEEE Trans. Signal Process. 48(5), 1338\u20131353 (2000)","journal-title":"IEEE Trans. Signal Process."},{"issue":"12","key":"40_CR26","doi-asserted-by":"publisher","first-page":"2355","DOI":"10.1364\/JOSAA.17.002355","volume":"17","author":"S-C Pei","year":"2000","unstructured":"Pei, S.-C., Ding, J.-J.: Simplified fractional Fourier transforms. JOSA A 17(12), 2355\u20132367 (2000)","journal-title":"JOSA A"},{"key":"40_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Zhang, L.: Learning a single convolutional super-resolution network for multiple degradations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3262\u20133271 (2018)","DOI":"10.1109\/CVPR.2018.00344"},{"key":"40_CR28","doi-asserted-by":"crossref","unstructured":"Gu, J., Lu, H., Zuo, W., Dong, C.: Blind super-resolution with iterative kernel correction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1604\u20131613 (2019)","DOI":"10.1109\/CVPR.2019.00170"},{"key":"40_CR29","unstructured":"Rerabek, M., Ebrahimi, T.: New light field image dataset. In: 8th International Conference on Quality of Multimedia Experience (QoMEX) (2016)"},{"key":"40_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/978-3-319-54187-7_2","volume-title":"Computer Vision \u2013 ACCV 2016","author":"K Honauer","year":"2017","unstructured":"Honauer, K., Johannsen, O., Kondermann, D., Goldluecke, B.: A dataset and evaluation methodology for depth estimation on 4D\u00a0light fields. In: Lai, S.-H., Lepetit, V., Nishino, K., Sato, Y. (eds.) ACCV 2016, Part III. LNCS, vol. 10113, pp. 19\u201334. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-54187-7_2"},{"key":"40_CR31","unstructured":"Wanner, S., Meister, S., Goldluecke, B.: Datasets and benchmarks for densely sampled 4D light fields. In: VMV, vol. 13, pp. 225\u2013226 (2013)"},{"issue":"4","key":"40_CR32","doi-asserted-by":"publisher","first-page":"1981","DOI":"10.1109\/TIP.2018.2791864","volume":"27","author":"M Le Pendu","year":"2018","unstructured":"Le Pendu, M., Jiang, X., Guillemot, C.: Light field inpainting propagation via low rank matrix completion. IEEE Trans. Image Process. 27(4), 1981\u20131993 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"40_CR33","unstructured":"Vaish, V.,\u00a0Adams, A.: The (new) Stanford light field archive, computer graphics laboratory (2008)"},{"issue":"4","key":"40_CR34","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"40_CR35","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"key":"40_CR36","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646\u20131654 (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"40_CR37","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S.,\u00a0Lee, K.M.: 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 (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"issue":"5","key":"40_CR38","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1109\/TIP.2018.2885236","volume":"28","author":"HWF Yeung","year":"2019","unstructured":"Yeung, H.W.F., Hou, J., Chen, X., Chen, J., Chen, Z., Chung, Y.Y.: Light field spatial super-resolution using deep efficient spatial-angular separable convolution. IEEE Trans. Image Process. 28(5), 2319\u20132330 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"40_CR39","doi-asserted-by":"publisher","unstructured":"Wang, Y., Wang, L., Yang, J., An, W., Yu, J., Guo, Y.: Spatial-angular interaction for light field image super-resolution. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020, Part XXIII. LNCS, vol. 12368, pp. 290\u2013308. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58592-1_18","DOI":"10.1007\/978-3-030-58592-1_18"},{"issue":"12","key":"40_CR40","first-page":"63","volume":"51","author":"H Li","year":"2024","unstructured":"Li, H., Lv, T., Yingchun, W., Chen, J.: A light field image super-resolution network based on dual-path guided update. Opto-Electron. Eng. 51(12), 63\u201375 (2024)","journal-title":"Opto-Electron. Eng."},{"issue":"12","key":"40_CR41","first-page":"4113","volume":"52","author":"J Chen","year":"2024","unstructured":"Chen, J., Yingchun, W., Lyu, T., Liu, L., Zhao, X.: Light field image super-resolution network based on interleaved feature update. Acta Electron. Sin. 52(12), 4113\u20134124 (2024)","journal-title":"Acta Electron. Sin."}],"container-title":["Lecture Notes in Computer Science","Image and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3398-5_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T23:20:00Z","timestamp":1761952800000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3398-5_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,1]]},"ISBN":["9789819533978","9789819533985"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3398-5_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,1]]},"assertion":[{"value":"1 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIG","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image and Graphics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xuzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icig2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icig.csig.org.cn\/2025\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}