{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T14:55:48Z","timestamp":1743000948957,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":11,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819629107"},{"type":"electronic","value":"9789819629114"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-2911-4_14","type":"book-chapter","created":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T09:43:45Z","timestamp":1741599825000},"page":"138-145","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Stereo Image Super-Resolution via Disparity Estimation and Domain Diffusion"],"prefix":"10.1007","author":[{"given":"Wanjun","family":"Wang","sequence":"first","affiliation":[]},{"given":"Chunyan","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Hongjun","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Kai","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Huihui","family":"Han","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,11]]},"reference":[{"key":"14_CR1","unstructured":"Zhang, D., Huang, F., Liu, S., Wang, X., Jin, Z.: Swinfir: revisiting the swinIR with fast fourier convolution and improved training for image super-resolution, arXiv preprint arXiv:2208.11247 (2022)"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Chen, L,, Chu, X., Zhang., X., et al.: Simple baselines for image restoration. In: European Conference on Computer Vision (ECCV), pp. 17\u201333. Springer, Israel (2022)","DOI":"10.1007\/978-3-031-20071-7_2"},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Chu, X., Chen, L., Yu, W.: Nafssr: stereo image super-resolution using nafnet. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1239\u20131248. IEEE, New Orleans (2022)","DOI":"10.1109\/CVPRW56347.2022.00130"},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J., Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1646\u20131654. IEEE, Las Vegas (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"14_CR5","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), pp. 136\u2013144. IEEE, Hawaii (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2472\u20132481. IEEE, Utah (2018)","DOI":"10.1109\/CVPR.2018.00262"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Dai, Q., Li, J., Yi, Q., Fang, F., Zhang, G.: Feedback network for mutually boosted stereo image super-resolution and disparity estimation. In: Proceedings of 29th ACM International Conference Multimedia (MM), pp. 1985\u20131993. ACM, Chengdu (2021)","DOI":"10.1145\/3474085.3475356"},{"key":"14_CR8","doi-asserted-by":"publisher","first-page":"1404","DOI":"10.1109\/ACCESS.2018.2886528","volume":"7","author":"P Zheng","year":"2019","unstructured":"Zheng, P., Askham, T., Brunton, S.L., Kutz, J.N., Aravkin, A.Y.: A unified framework for sparse relaxed regularized regression: sr3. IEEE Access 7, 1404\u20131423 (2019)","journal-title":"IEEE Access"},{"key":"14_CR9","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Proceedings of the 34th Neural Information Processing System (NIPS), pp. 6840\u20136851. MIT Press, NY (2020)"},{"key":"14_CR10","unstructured":"Wang, Y., Yu, J., Zhang, J.: Zero-shot image restoration using denoising diffusion null-space model, arXiv preprint arXiv:2212.00490, (2022)"},{"key":"14_CR11","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 IEEE Conference on Computer Vision (ECCV), pp. 286\u2013301. Springer, Munich (2018)","DOI":"10.1007\/978-3-030-01234-2_18"}],"container-title":["Communications in Computer and Information Science","Artificial Intelligence and Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-2911-4_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T09:45:58Z","timestamp":1741599958000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-2911-4_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819629107","9789819629114"],"references-count":11,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-2911-4_14","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"11 March 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISAIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Artificial Intelligence and Robotics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guilin","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isair2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/isair.site\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}