{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T15:02:40Z","timestamp":1786978960052,"version":"build-2736575974"},"publisher-location":"Cham","reference-count":85,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031736605","type":"print"},{"value":"9783031736612","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,10]],"date-time":"2024-11-10T00:00:00Z","timestamp":1731196800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,10]],"date-time":"2024-11-10T00:00:00Z","timestamp":1731196800000},"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\/978-3-031-73661-2_25","type":"book-chapter","created":{"date-parts":[[2024,11,9]],"date-time":"2024-11-09T06:08:01Z","timestamp":1731132481000},"page":"446-464","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["SPIRE: Semantic Prompt-Driven Image Restoration"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6462-6534","authenticated-orcid":false,"given":"Chenyang","family":"Qi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7594-2292","authenticated-orcid":false,"given":"Zhengzhong","family":"Tu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7349-7762","authenticated-orcid":false,"given":"Keren","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7539-2991","authenticated-orcid":false,"given":"Mauricio","family":"Delbracio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peyman","family":"Milanfar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2199-3948","authenticated-orcid":false,"given":"Qifeng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hossein","family":"Talebi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,10]]},"reference":[{"key":"25_CR1","doi-asserted-by":"crossref","unstructured":"Abuolaim, A., Delbracio, M., Kelly, D., Brown, M.S., Milanfar, P.: Learning to reduce defocus blur by realistically modeling dual-pixel data. In: ICCV, pp. 2289\u20132298 (2021)","DOI":"10.1109\/ICCV48922.2021.00229"},{"key":"25_CR2","doi-asserted-by":"publisher","unstructured":"Agustsson, E., Timofte, R.: NTIRE 2017 challenge on single image super-resolution: dataset and study. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2017, Honolulu, HI, USA, 21\u201326 July 2017, pp. 1122\u20131131. IEEE Computer Society (2017). https:\/\/doi.org\/10.1109\/CVPRW.2017.150","DOI":"10.1109\/CVPRW.2017.150"},{"issue":"4","key":"25_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3592450","volume":"42","author":"O Avrahami","year":"2023","unstructured":"Avrahami, O., Fried, O., Lischinski, D.: Blended latent diffusion. ACM Trans. Graph. 42(4), 1\u201311 (2023)","journal-title":"ACM Trans. Graph."},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Avrahami, O., Lischinski, D., Fried, O.: Blended diffusion for text-driven editing of natural images. In: CVPR, pp. 18208\u201318218 (2022)","DOI":"10.1109\/CVPR52688.2022.01767"},{"key":"25_CR5","unstructured":"Bai, Y., Wang, C., Xie, S., Dong, C., Yuan, C., Wang, Z.: TextIR: a simple framework for text-based editable image restoration. arXiv preprint arXiv:2302.14736 (2023)"},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Blau, Y., Michaeli, T.: The perception-distortion tradeoff. In: CVPR, pp. 6228\u20136237 (2018)","DOI":"10.1109\/CVPR.2018.00652"},{"key":"25_CR7","doi-asserted-by":"crossref","unstructured":"Brooks, T., Holynski, A., Efros, A.A.: InstructPix2Pix: learning to follow image editing instructions. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.01764"},{"key":"25_CR8","unstructured":"Chen, X., et al.: PaLI: a jointly-scaled multilingual language-image model. In: ICLR (2023). https:\/\/arxiv.org\/abs\/2209.06794"},{"key":"25_CR9","unstructured":"Chen, Z., et al.: Image super-resolution with text prompt diffusion. arXiv preprint arXiv:2303.06373 (2023)"},{"key":"25_CR10","unstructured":"Couairon, G., Verbeek, J., Schwenk, H., Cord, M.: DiffEdit: diffusion-based semantic image editing with mask guidance. In: ICLR (2022)"},{"key":"25_CR11","unstructured":"Delbracio, M., Milanfar, P.: Inversion by direct iteration: an alternative to denoising diffusion for image restoration. Trans. Mach. Learn. Res. (2023). https:\/\/openreview.net\/forum?id=VmyFF5lL3F. Featured Certification"},{"key":"25_CR12","doi-asserted-by":"crossref","unstructured":"Delbracio, M., Talebei, H., Milanfar, P.: Projected distribution loss for image enhancement. In: 2021 IEEE International Conference on Computational Photography (ICCP), pp. 1\u201312. IEEE (2021)","DOI":"10.1109\/ICCP51581.2021.9466271"},{"key":"25_CR13","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. In: Neural Information Processing Systems (2021)"},{"issue":"2","key":"25_CR14","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2016","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295\u2013307 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"25_CR15","unstructured":"Gal, R., et al.: An image is worth one word: personalizing text-to-image generation using textual inversion. In: ICLR (2023)"},{"key":"25_CR16","doi-asserted-by":"crossref","unstructured":"Galteri, L., Seidenari, L., Bertini, M., Bimbo, A.: Deep generative adversarial compression artifact removal. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.517"},{"key":"25_CR17","doi-asserted-by":"publisher","unstructured":"Geng, Z., et al.: InstructDiffusion: a generalist modeling interface for vision tasks. CoRR abs\/2309.03895 (2023). https:\/\/doi.org\/10.48550\/arXiv.2309.03895","DOI":"10.48550\/arXiv.2309.03895"},{"key":"25_CR18","unstructured":"Gu, J., et al.: NTIRE 2022 challenge on perceptual image quality assessment. In: CVPRW, pp. 951\u2013967 (2022)"},{"key":"25_CR19","unstructured":"Gu, X., Lin, T.Y., Kuo, W., Cui, Y.: Open-vocabulary object detection via vision and language knowledge distillation. In: ICLR (2021)"},{"key":"25_CR20","doi-asserted-by":"crossref","unstructured":"Han, L., Li, Y., Zhang, H., Milanfar, P., Metaxas, D., Yang, F.: SVDiff: compact parameter space for diffusion fine-tuning. In: ICCV, pp. 7323\u20137334 (2023)","DOI":"10.1109\/ICCV51070.2023.00673"},{"key":"25_CR21","unstructured":"Hertz, A., Mokady, R., Tenenbaum, J., Aberman, K., Pritch, Y., Cohen-or, D.: Prompt-to-prompt image editing with cross-attention control. In: ICLR (2023). https:\/\/openreview.net\/forum?id=_CDixzkzeyb"},{"key":"25_CR22","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In: NeurIPS (2017)"},{"key":"25_CR23","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: NeurIPS, vol. 33, pp. 6840\u20136851 (2020)"},{"key":"25_CR24","unstructured":"Ho, J., Salimans, T.: Classifier-free diffusion guidance. In: NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications (2021). https:\/\/openreview.net\/forum?id=qw8AKxfYbI"},{"key":"25_CR25","unstructured":"Hu, E.J., et al.: LoRA: low-rank adaptation of large language models. In: ICLR (2022). https:\/\/openreview.net\/forum?id=nZeVKeeFYf9"},{"key":"25_CR26","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Zhang, Z., Xue, T., Gu, J.: AutoDIR: automatic all-in-one image restoration with latent diffusion. arXiv preprint arXiv:2310.10123 (2023)","DOI":"10.1007\/978-3-031-73661-2_19"},{"key":"25_CR27","doi-asserted-by":"crossref","unstructured":"Ke, J., Wang, Q., Wang, Y., Milanfar, P., Yang, F.: MUSIQ: multi-scale image quality transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5148\u20135157 (2021)","DOI":"10.1109\/ICCV48922.2021.00510"},{"key":"25_CR28","doi-asserted-by":"crossref","unstructured":"Ke, J., Ye, K., Yu, J., Wu, Y., Milanfar, P., Yang, F.: VILA: learning image aesthetics from user comments with vision-language pretraining. In: CVPR, pp. 10041\u201310051 (2023)","DOI":"10.1109\/CVPR52729.2023.00968"},{"key":"25_CR29","doi-asserted-by":"crossref","unstructured":"Kumari, N., Zhang, B., Zhang, R., Shechtman, E., Zhu, J.Y.: Multi-concept customization of text-to-image diffusion. In: CVPR, pp. 1931\u20131941 (2023)","DOI":"10.1109\/CVPR52729.2023.00192"},{"key":"25_CR30","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Gool, L.V., Timofte, R.: SwinIR: image restoration using swin transformer. In: Proceedings of ICCV Workshops (2021)","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"25_CR31","doi-asserted-by":"crossref","unstructured":"Liang, Z., Li, C., Zhou, S., Feng, R., Loy, C.C.: Iterative prompt learning for unsupervised backlit image enhancement. In: ICCV, pp. 8094\u20138103 (2023)","DOI":"10.1109\/ICCV51070.2023.00743"},{"key":"25_CR32","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: Proceedings of CVPR Workshops (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"25_CR33","doi-asserted-by":"publisher","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48. https:\/\/www.microsoft.com\/en-us\/research\/publication\/microsoft-coco-common-objects-in-context\/","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"25_CR34","doi-asserted-by":"crossref","unstructured":"Lin, X., et al.: DiffBIR: towards blind image restoration with generative diffusion prior. arXiv preprint arXiv:2308.15070 (2023)","DOI":"10.1007\/978-3-031-73202-7_25"},{"key":"25_CR35","unstructured":"Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual instruction tuning. In: NeurIPS (2023)"},{"key":"25_CR36","unstructured":"Luo, Z., Gustafsson, F.K., Zhao, Z., Sj\u00f6lund, J., Sch\u00f6n, T.B.: Controlling vision-language models for multi-task image restoration. In: The Twelfth International Conference on Learning Representations (2024). https:\/\/openreview.net\/forum?id=t3vnnLeajU"},{"key":"25_CR37","unstructured":"Meng, C., et al.: SDEdit: guided image synthesis and editing with stochastic differential equations. In: ICLR (2022)"},{"key":"25_CR38","doi-asserted-by":"crossref","unstructured":"Mokady, R., Hertz, A., Aberman, K., Pritch, Y., Cohen-Or, D.: Null-text inversion for editing real images using guided diffusion models. In: CVPR, pp. 6038\u20136047 (2023)","DOI":"10.1109\/CVPR52729.2023.00585"},{"key":"25_CR39","doi-asserted-by":"crossref","unstructured":"Mou, C., et al.: T2I-Adapter: learning adapters to dig out more controllable ability for text-to-image diffusion models. arXiv preprint arXiv:2302.08453 (2023)","DOI":"10.1609\/aaai.v38i5.28226"},{"key":"25_CR40","doi-asserted-by":"publisher","unstructured":"Nah, S., et al.: NTIRE 2019 challenge on video deblurring: methods and results. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2019, Long Beach, CA, USA, 16\u201320 June 2019, pp. 1974\u20131984. Computer Vision Foundation\/IEEE (2019). https:\/\/doi.org\/10.1109\/CVPRW.2019.00249. http:\/\/openaccess.thecvf.com\/content_CVPRW_2019\/html\/NTIRE\/Nah_NTIRE_2019_Challenge_on_Video_Deblurring_Methods_and_Results_CVPRW_2019_paper.html","DOI":"10.1109\/CVPRW.2019.00249"},{"key":"25_CR41","unstructured":"OpenAI: GPT-4 technical report (2023)"},{"key":"25_CR42","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1007\/978-3-031-19775-8_20","volume-title":"ECCV 2022","author":"R Paiss","year":"2022","unstructured":"Paiss, R., Chefer, H., Wolf, L.: No token left behind: explainability-aided image classification and generation. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13672, pp. 334\u2013350. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19775-8_20"},{"key":"25_CR43","doi-asserted-by":"crossref","unstructured":"Paiss, R., et al.: Teaching CLIP to count to ten. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.00294"},{"key":"25_CR44","doi-asserted-by":"crossref","unstructured":"Parmar, G., Kumar\u00a0Singh, K., Zhang, R., Li, Y., Lu, J., Zhu, J.Y.: Zero-shot image-to-image translation. In: ACM SIGGRAPH 2023 Conference Proceedings, pp. 1\u201311 (2023)","DOI":"10.1145\/3588432.3591513"},{"key":"25_CR45","unstructured":"Prakash, M., Delbracio, M., Milanfar, P., Jug, F.: Interpretable unsupervised diversity denoising and artefact removal. In: ICLR (2022). https:\/\/openreview.net\/forum?id=DfMqlB0PXjM"},{"key":"25_CR46","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"issue":"8","key":"25_CR47","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI Blog 1(8), 9 (2019)","journal-title":"OpenAI Blog"},{"key":"25_CR48","unstructured":"Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., Chen, M.: Hierarchical text-conditional image generation with CLIP latents. arXiv preprint arXiv:2204.06125 (2022)"},{"key":"25_CR49","doi-asserted-by":"crossref","unstructured":"Ren, M., Delbracio, M., Talebi, H., Gerig, G., Milanfar, P.: Multiscale structure guided diffusion for image deblurring. In: ICCV, pp. 10721\u201310733 (2023)","DOI":"10.1109\/ICCV51070.2023.00984"},{"key":"25_CR50","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: CVPR, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"25_CR51","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2014 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"25_CR52","doi-asserted-by":"crossref","unstructured":"Saharia, C., et al.: Palette: image-to-image diffusion models. In: ACM SIGGRAPH 2022 Conference Proceedings, pp. 1\u201310 (2022)","DOI":"10.1145\/3528233.3530757"},{"key":"25_CR53","unstructured":"Saharia, C., et\u00a0al.: Photorealistic text-to-image diffusion models with deep language understanding. In: NeurIPS (2022)"},{"issue":"4","key":"25_CR54","first-page":"4713","volume":"45","author":"C Saharia","year":"2022","unstructured":"Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D.J., Norouzi, M.: Image super-resolution via iterative refinement. IEEE Trans. Pattern Anal. Mach. Intell. 45(4), 4713\u20134726 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"25_CR55","unstructured":"Salimans, T., Ho, J.: Progressive distillation for fast sampling of diffusion models. In: ICLR. OpenReview.net (2022). https:\/\/openreview.net\/forum?id=TIdIXIpzhoI"},{"key":"25_CR56","unstructured":"Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)"},{"key":"25_CR57","unstructured":"Song, Y., Ermon, S.: Generative modeling by estimating gradients of the data distribution. In: NeurIPS, pp. 11895\u201311907 (2019)"},{"key":"25_CR58","unstructured":"Song, Y., Shen, L., Xing, L., Ermon, S.: Solving inverse problems in medical imaging with score-based generative models. In: ICLR. OpenReview.net (2022)"},{"key":"25_CR59","unstructured":"Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. In: ICLR (2021). https:\/\/openreview.net\/forum?id=PxTIG12RRHS"},{"key":"25_CR60","doi-asserted-by":"crossref","unstructured":"Su, S., Delbracio, M., Wang, J., Sapiro, G., Heidrich, W., Wang, O.: Deep video deblurring for hand-held cameras. In: CVPR, pp. 1279\u20131288 (2017)","DOI":"10.1109\/CVPR.2017.33"},{"key":"25_CR61","doi-asserted-by":"crossref","unstructured":"Sun, H., et al.: CoSeR: bridging image and language for cognitive super-resolution. arXiv preprint arXiv:2311.16512 (2023)","DOI":"10.1109\/CVPR52733.2024.02444"},{"key":"25_CR62","doi-asserted-by":"crossref","unstructured":"Tu, Z., et al.: MAXIM: multi-axis MLP for image processing. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00568"},{"key":"25_CR63","doi-asserted-by":"crossref","unstructured":"Tumanyan, N., Geyer, M., Bagon, S., Dekel, T.: Plug-and-play diffusion features for text-driven image-to-image translation. In: CVPR, pp. 1921\u20131930 (2023)","DOI":"10.1109\/CVPR52729.2023.00191"},{"key":"25_CR64","unstructured":"Vaswani, A., et al.: Attention is all you need. In: NeurIPS, vol. 30 (2017)"},{"key":"25_CR65","doi-asserted-by":"crossref","unstructured":"Wang, J., Chan, K.C., Loy, C.C.: Exploring clip for assessing the look and feel of images. In: AAAI (2023)","DOI":"10.1609\/aaai.v37i2.25353"},{"key":"25_CR66","doi-asserted-by":"crossref","unstructured":"Wang, J., Yue, Z., Zhou, S., Chan, K.C., Loy, C.C.: Exploiting diffusion prior for real-world image super-resolution. arXiv preprint arXiv:2305.07015 (2023)","DOI":"10.1007\/s11263-024-02168-7"},{"key":"25_CR67","doi-asserted-by":"crossref","unstructured":"Wang, L., Wang, Y., Lin, Z., Yang, J., An, W., Guo, Y.: Learning a single network for scale-arbitrary super-resolution. In: ICCV, pp. 4801\u20134810 (2021)","DOI":"10.1109\/ICCV48922.2021.00476"},{"key":"25_CR68","doi-asserted-by":"crossref","unstructured":"Wang, X., Chen, X., Ni, B., Wang, H., Tong, Z., Liu, Y.: Deep arbitrary-scale image super-resolution via scale-equivariance pursuit. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.00178"},{"key":"25_CR69","doi-asserted-by":"crossref","unstructured":"Wang, X., Li, Y., Zhang, H., Shan, Y.: Towards real-world blind face restoration with generative facial prior. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00905"},{"key":"25_CR70","doi-asserted-by":"crossref","unstructured":"Wang, X., Xie, L., Dong, C., Shan, Y.: Real-ESRGAN: training real-world blind super-resolution with pure synthetic data. In: ICCV, pp. 1905\u20131914 (2021)","DOI":"10.1109\/ICCVW54120.2021.00217"},{"key":"25_CR71","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-030-11021-5_5","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops","author":"X Wang","year":"2019","unstructured":"Wang, X., et al.: ESRGAN: enhanced super-resolution generative adversarial networks. In: Leal-Taix\u00e9, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11133, pp. 63\u201379. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11021-5_5"},{"key":"25_CR72","doi-asserted-by":"crossref","unstructured":"Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., Li, H.: Uformer: a general U-shaped transformer for image restoration. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01716"},{"key":"25_CR73","doi-asserted-by":"crossref","unstructured":"Whang, J., Delbracio, M., Talebi, H., Saharia, C., Dimakis, A.G., Milanfar, P.: Deblurring via stochastic refinement. In: CVPR, pp. 16293\u201316303 (2022)","DOI":"10.1109\/CVPR52688.2022.01581"},{"key":"25_CR74","doi-asserted-by":"crossref","unstructured":"Wu, R., Yang, T., Sun, L., Zhang, Z., Li, S., Zhang, L.: SeeSR: towards semantics-aware real-world image super-resolution. In: CVPR (2024)","DOI":"10.1109\/CVPR52733.2024.02405"},{"key":"25_CR75","doi-asserted-by":"crossref","unstructured":"Yang, S., et al.: MANIQA: multi-dimension attention network for no-reference image quality assessment. In: CVPR, pp. 1191\u20131200 (2022)","DOI":"10.1109\/CVPRW56347.2022.00126"},{"key":"25_CR76","doi-asserted-by":"crossref","unstructured":"Yang, T., Ren, P., Xie, X., Zhang, L.: GAN prior embedded network for blind face restoration in the wild. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00073"},{"key":"25_CR77","doi-asserted-by":"crossref","unstructured":"Yu, F., et al.: Scaling up to excellence: practicing model scaling for photo-realistic image restoration in the wild. In: CVPR (2024)","DOI":"10.1109\/CVPR52733.2024.02425"},{"key":"25_CR78","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., et al.: Multi-stage progressive image restoration. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01458"},{"key":"25_CR79","doi-asserted-by":"crossref","unstructured":"Zhang, K., Liang, J., Van\u00a0Gool, L., Timofte, R.: Designing a practical degradation model for deep blind image super-resolution. In: ICCV, pp. 4791\u20134800 (2021)","DOI":"10.1109\/ICCV48922.2021.00477"},{"key":"25_CR80","unstructured":"Zhang, K., Mo, L., Chen, W., Sun, H., Su, Y.: MagicBrush: a manually annotated dataset for instruction-guided image editing. In: Advances in Neural Information Processing Systems, vol. 36 (2024)"},{"issue":"7","key":"25_CR81","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","volume":"26","author":"K Zhang","year":"2017","unstructured":"Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142\u20133155 (2017)","journal-title":"IEEE Trans. Image Process."},{"issue":"9","key":"25_CR82","doi-asserted-by":"publisher","first-page":"4608","DOI":"10.1109\/TIP.2018.2839891","volume":"27","author":"K Zhang","year":"2018","unstructured":"Zhang, K., Zuo, W., Zhang, L.: FFDNet: toward a fast and flexible solution for CNN based image denoising. IEEE Trans. Image Process. 27(9), 4608\u20134622 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"25_CR83","doi-asserted-by":"crossref","unstructured":"Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.00355"},{"key":"25_CR84","unstructured":"Zhang, R., et al.: Tip-Adapter: training-free CLIP-adapter for better vision-language modeling. In: ECCV (2022)"},{"key":"25_CR85","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00068"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73661-2_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,30]],"date-time":"2024-11-30T21:17:36Z","timestamp":1733001456000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73661-2_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,10]]},"ISBN":["9783031736605","9783031736612"],"references-count":85,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73661-2_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,10]]},"assertion":[{"value":"10 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}