{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T22:20:11Z","timestamp":1757542811568,"version":"3.40.3"},"publisher-location":"Cham","reference-count":75,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031197994"},{"type":"electronic","value":"9783031198007"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-19800-7_13","type":"book-chapter","created":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T12:09:38Z","timestamp":1667909378000},"page":"217-234","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Learning Discriminative Shrinkage Deep Networks for\u00a0Image Deconvolution"],"prefix":"10.1007","author":[{"given":"Pin-Hung","family":"Kuo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinshan","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shao-Yi","family":"Chien","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming-Hsuan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,9]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Aljadaany, R., Pal, D.K., Savvides, M.: Douglas-rachford networks: learning both the image prior and data fidelity terms for blind image deconvolution. In: CVPR, pp. 10235\u201310244 (2019)","DOI":"10.1109\/CVPR.2019.01048"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Barrett, R., et al.: Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods. SIAM (1994)","DOI":"10.1137\/1.9781611971538"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Bevilacqua, M., Roumy, A., Guillemot, C., Alberi-Morel, M.L.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: BMVC (2012)","DOI":"10.5244\/C.26.135"},{"key":"13_CR4","unstructured":"Bigdeli, S.A., Zwicker, M., Favaro, P., Jin, M.: Deep mean-shift priors for image restoration. In: NeurIPS (2017)"},{"key":"13_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/978-3-319-46487-9_14","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Chakrabarti","year":"2016","unstructured":"Chakrabarti, A.: A neural approach to blind motion deblurring. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 221\u2013235. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_14"},{"issue":"3","key":"13_CR6","first-page":"370","volume":"7","author":"TF Chan","year":"1998","unstructured":"Chan, T.F., Wong, C.K.: Total variation blind deconvolution. IEEE TIP 7(3), 370\u2013375 (1998)","journal-title":"IEEE TIP"},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhang, J., Pan, J., Lin, S., Fang, F., Ren, J.S.: Learning a non-blind deblurring network for night blurry images. In: CVPR, pp. 10542\u201310550 (2021)","DOI":"10.1109\/CVPR46437.2021.01040"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Cho, S., Wang, J., Lee, S.: Handling outliers in non-blind image deconvolution. In: ICCV, pp. 495\u2013502 (2011)","DOI":"10.1109\/ICCV.2011.6126280"},{"key":"13_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1007\/978-3-030-01252-6_46","volume-title":"Computer Vision \u2013 ECCV 2018","author":"J Dong","year":"2018","unstructured":"Dong, J., Pan, J., Sun, D., Su, Z., Yang, M.-H.: Learning data terms for non-blind deblurring. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 777\u2013792. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01252-6_46"},{"key":"13_CR10","unstructured":"Dong, J., Roth, S., Schiele, B.: Deep wiener deconvolution: Wiener meets deep learning for image deblurring. In: NeurIPS (2020)"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Dong, J., Roth, S., Schiele, B.: Learning spatially-variant map models for non-blind image deblurring. In: CVPR, pp. 4886\u20134895 (2021)","DOI":"10.1109\/CVPR46437.2021.00485"},{"key":"13_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1007\/978-3-030-58520-4_19","volume-title":"Computer Vision \u2013 ECCV 2020","author":"T Eboli","year":"2020","unstructured":"Eboli, T., Sun, J., Ponce, J.: End-to-end interpretable learning of non-blind image deblurring. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12362, pp. 314\u2013331. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58520-4_19"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Gao, H., Tao, X., Shen, X., Jia, J.: Dynamic scene deblurring with parameter selective sharing and nested skip connections. In: CVPR, pp. 3848\u20133856 (2019)","DOI":"10.1109\/CVPR.2019.00397"},{"issue":"3","key":"13_CR14","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1109\/34.120331","volume":"14","author":"D Geman","year":"1992","unstructured":"Geman, D., Reynolds, G.: Constrained restoration and the recovery of discontinuities. IEEE TPAMI 14(3), 367\u2013383 (1992)","journal-title":"IEEE TPAMI"},{"issue":"12","key":"13_CR15","doi-asserted-by":"publisher","first-page":"5468","DOI":"10.1109\/TNNLS.2020.2968289","volume":"31","author":"D Gong","year":"2020","unstructured":"Gong, D., Zhang, Z., Shi, Q., van den Hengel, A., Shen, C., Zhang, Y.: Learning deep gradient descent optimization for image deconvolution. IEEE Trans. Neural Netw. Learn. Syst. 31(12), 5468\u20135482 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"13_CR16","unstructured":"Goodfellow, I., Warde-Farley, D., Mirza, M., Courville, A., Bengio, Y.: Maxout networks. In: ICML, pp. 1319\u20131327 (2013)"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Gu, S., Zhang, L., Zuo, W., Feng, X.: Weighted nuclear norm minimization with application to image denoising. In: CVPR, pp. 2862\u20132869 (2014)","DOI":"10.1109\/CVPR.2014.366"},{"key":"13_CR18","unstructured":"Hadamard, J.: Lectures on Cauchy\u2019s Problem in Linear Partial Differential Equations. Courier Corporation (2003)"},{"key":"13_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1007\/978-3-642-33786-4_9","volume-title":"Computer Vision \u2013 ECCV 2012","author":"J Jancsary","year":"2012","unstructured":"Jancsary, J., Nowozin, S., Rother, C.: Loss-specific training of non-parametric image restoration models: a new state of the art. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7578, pp. 112\u2013125. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33786-4_9"},{"key":"13_CR20","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Ko, H.C., Chang, J.Y., Ding, J.J.: Deep priors inside an unrolled and adaptive deconvolution model. In: ACCV (2020)","DOI":"10.1007\/978-3-030-69532-3_23"},{"key":"13_CR22","first-page":"852","volume":"31","author":"S Kong","year":"2021","unstructured":"Kong, S., Wang, W., Feng, X., Jia, X.: Deep red unfolding network for image restoration. IEEE TIP 31, 852\u2013867 (2021)","journal-title":"IEEE TIP"},{"key":"13_CR23","unstructured":"Krishnan, D., Fergus, R.: Fast image deconvolution using hyper-Laplacian priors. In: NeurIPS, pp. 1033\u20131041 (2009)"},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"Krishnan, D., Tay, T., Fergus, R.: Blind deconvolution using a normalized sparsity measure. In: CVPR, pp. 233\u2013240 (2011)","DOI":"10.1109\/CVPR.2011.5995521"},{"key":"13_CR25","doi-asserted-by":"crossref","unstructured":"Kruse, J., Rother, C., Schmidt, U.: Learning to push the limits of efficient FFT-based image deconvolution. In: ICCV, pp. 4586\u20134594 (2017)","DOI":"10.1109\/ICCV.2017.491"},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"Kupyn, O., Budzan, V., Mykhailych, M., Mishkin, D., Matas, J.: Deblurgan: Blind motion deblurring using conditional adversarial networks. In: CVPR, pp. 8183\u20138192 (2018)","DOI":"10.1109\/CVPR.2018.00854"},{"key":"13_CR27","doi-asserted-by":"crossref","unstructured":"Lai, W.S., Huang, J.B., Hu, Z., Ahuja, N., Yang, M.H.: A comparative study for single image blind deblurring. In: CVPR, pp. 1701\u20131709 (2016)","DOI":"10.1109\/CVPR.2016.188"},{"key":"13_CR28","doi-asserted-by":"crossref","unstructured":"Levin, A., Fergus, R., Durand, F., Freeman, W.T.: Image and depth from a conventional camera with a coded aperture. ACM TOG. 26(3), 70-es (2007)","DOI":"10.1145\/1276377.1276464"},{"issue":"9","key":"13_CR29","doi-asserted-by":"publisher","first-page":"1647","DOI":"10.1109\/TPAMI.2007.1106","volume":"29","author":"A Levin","year":"2007","unstructured":"Levin, A., Weiss, Y.: User assisted separation of reflections from a single image using a sparsity prior. IEEE TPAMI 29(9), 1647\u20131654 (2007)","journal-title":"IEEE TPAMI"},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Understanding and evaluating blind deconvolution algorithms. In: CVPR, pp. 1964\u20131971 (2009)","DOI":"10.1109\/CVPR.2009.5206815"},{"issue":"12","key":"13_CR31","doi-asserted-by":"publisher","first-page":"2354","DOI":"10.1109\/TPAMI.2011.148","volume":"33","author":"A Levin","year":"2011","unstructured":"Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Understanding blind deconvolution algorithms. IEEE TPAMI 33(12), 2354\u20132367 (2011)","journal-title":"IEEE TPAMI"},{"issue":"8","key":"13_CR32","doi-asserted-by":"publisher","first-page":"1025","DOI":"10.1007\/s11263-018-01146-0","volume":"127","author":"L Li","year":"2019","unstructured":"Li, L., Pan, J., Lai, W.S., Gao, C., Sang, N., Yang, M.H.: Blind image deblurring via deep discriminative priors. IJCV 127(8), 1025\u20131043 (2019)","journal-title":"IJCV"},{"key":"13_CR33","doi-asserted-by":"crossref","unstructured":"Li, Y., Tofighi, M., Geng, J., Monga, V., Eldar, Y.C.: Deep algorithm unrolling for blind image deblurring. arXiv preprint arXiv:1902.03493 (2019)","DOI":"10.1109\/ICASSP.2019.8682542"},{"key":"13_CR34","first-page":"5","volume":"2012","author":"CS Liu","year":"2012","unstructured":"Liu, C.S.: Modifications of steepest descent method and conjugate gradient method against noise for ill-posed linear systems. Commun. Numer. Anal. 2012, 5 (2012)","journal-title":"Commun. Numer. Anal."},{"key":"13_CR35","doi-asserted-by":"crossref","unstructured":"Liu, R., Jia, J.: Reducing boundary artifacts in image deconvolution. In: ICIP, pp. 505\u2013508 (2008)","DOI":"10.1109\/ICIP.2008.4711802"},{"issue":"2","key":"13_CR36","first-page":"1004","volume":"26","author":"K Ma","year":"2016","unstructured":"Ma, K., et al.: Waterloo exploration database: new challenges for image quality assessment models. IEEE TIP 26(2), 1004\u20131016 (2016)","journal-title":"IEEE TIP"},{"key":"13_CR37","first-page":"10","volume":"27","author":"L Marin","year":"2001","unstructured":"Marin, L., H\u00e1o, D.N., Lesnic, D.: Conjugate gradient-boundary element method for a Cauchy problem in the lam\u00e9 system. WIT Trans. Modell. Simul. 27, 10 (2001)","journal-title":"WIT Trans. Modell. Simul."},{"key":"13_CR38","doi-asserted-by":"crossref","unstructured":"Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: ICCV, pp. 416\u2013423 (2001)","DOI":"10.1109\/ICCV.2001.937655"},{"issue":"12","key":"13_CR39","doi-asserted-by":"publisher","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","volume":"21","author":"A Mittal","year":"2012","unstructured":"Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE TIP 21(12), 4695\u20134708 (2012). https:\/\/doi.org\/10.1109\/TIP.2012.2214050","journal-title":"IEEE TIP"},{"key":"13_CR40","doi-asserted-by":"crossref","unstructured":"Nah, S., Hyun Kim, T., Mu Lee, K.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: CVPR, pp. 3883\u20133891 (2017)","DOI":"10.1109\/CVPR.2017.35"},{"key":"13_CR41","doi-asserted-by":"crossref","unstructured":"Nan, Y., Ji, H.: Deep learning for handling kernel\/model uncertainty in image deconvolution. In: CVPR, pp. 2388\u20132397 (2020)","DOI":"10.1109\/CVPR42600.2020.00246"},{"key":"13_CR42","doi-asserted-by":"crossref","unstructured":"Nan, Y., Quan, Y., Ji, H.: Variational-em-based deep learning for noise-blind image deblurring. In: CVPR, pp. 3626\u20133635 (2020)","DOI":"10.1109\/CVPR42600.2020.00368"},{"key":"13_CR43","doi-asserted-by":"crossref","unstructured":"Pan, J., Sun, D., Pfister, H., Yang, M.H.: Blind image deblurring using dark channel prior. In: CVPR, pp. 1628\u20131636 (2016)","DOI":"10.1109\/CVPR.2016.180"},{"issue":"3","key":"13_CR44","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1561\/2400000003","volume":"1","author":"N Parikh","year":"2014","unstructured":"Parikh, N., Boyd, S.: Proximal algorithms. Found. Trends Optim. 1(3), 127\u2013239 (2014)","journal-title":"Found. Trends Optim."},{"key":"13_CR45","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019 Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) NeurIPS, pp. 8024\u20138035. Curran Associates, Inc. (2019). https:\/\/papers.neurips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf"},{"key":"13_CR46","doi-asserted-by":"crossref","unstructured":"Perrone, D., Favaro, P.: Total variation blind deconvolution: the devil is in the details. In: CVPR, pp. 2909\u20132916 (2014)","DOI":"10.1109\/CVPR.2014.372"},{"key":"13_CR47","unstructured":"Qiu, H., Hammernik, K., Qin, C., Rueckert, D.: GraDIRN: learning iterative gradient descent-based energy minimization for deformable image registration. arXiv preprint arXiv:2112.03915 (2021)"},{"key":"13_CR48","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1109\/TPAMI.2019.2926357","volume":"43","author":"D Ren","year":"2019","unstructured":"Ren, D., Zuo, W., Zhang, D., Zhang, L., Yang, M.H.: Simultaneous fidelity and regularization learning for image restoration. IEEE TPAMI 43, 284\u2013299 (2019)","journal-title":"IEEE TPAMI"},{"issue":"7","key":"13_CR49","first-page":"3426","volume":"25","author":"W Ren","year":"2016","unstructured":"Ren, W., Cao, X., Pan, J., Guo, X., Zuo, W., Yang, M.H.: Image deblurring via enhanced low-rank prior. IEEE TIP 25(7), 3426\u20133437 (2016)","journal-title":"IEEE TIP"},{"issue":"1","key":"13_CR50","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1364\/JOSA.62.000055","volume":"62","author":"WH Richardson","year":"1972","unstructured":"Richardson, W.H.: Bayesian-based iterative method of image restoration. JoSA 62(1), 55\u201359 (1972)","journal-title":"JoSA"},{"key":"13_CR51","doi-asserted-by":"crossref","unstructured":"Roth, S., Black, M.J.: Fields of experts: a framework for learning image priors. In: CVPR, pp. 860\u2013867 (2005)","DOI":"10.1109\/CVPR.2005.160"},{"key":"13_CR52","doi-asserted-by":"crossref","unstructured":"Rudin, L.I., Osher, S.: Total variation based image restoration with free local constraints. In: ICIP, vol. 1, pp. 31\u201335 (1994)","DOI":"10.1109\/ICIP.1994.413269"},{"key":"13_CR53","unstructured":"Ryabtsev, A.: The error accumulation in the conjugate gradient method for degenerate problem. arXiv preprint arXiv:2004.10242 (2020)"},{"key":"13_CR54","doi-asserted-by":"crossref","unstructured":"Samuel, K.G., Tappen, M.F.: Learning optimized map estimates in continuously-valued MRF models. In: CVPR, pp. 477\u2013484 (2009)","DOI":"10.1109\/CVPR.2009.5206774"},{"issue":"4","key":"13_CR55","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1109\/TPAMI.2015.2441053","volume":"38","author":"U Schmidt","year":"2015","unstructured":"Schmidt, U., Jancsary, J., Nowozin, S., Roth, S., Rother, C.: Cascades of regression tree fields for image restoration. IEEE TPAMI 38(4), 677\u2013689 (2015)","journal-title":"IEEE TPAMI"},{"key":"13_CR56","doi-asserted-by":"crossref","unstructured":"Schmidt, U., Roth, S.: Shrinkage fields for effective image restoration. In: CVPR, pp. 2774\u20132781 (2014)","DOI":"10.1109\/CVPR.2014.349"},{"key":"13_CR57","doi-asserted-by":"crossref","unstructured":"Schmidt, U., Rother, C., Nowozin, S., Jancsary, J., Roth, S.: Discriminative non-blind deblurring. In: CVPR, pp. 604\u2013611 (2013)","DOI":"10.1109\/CVPR.2013.84"},{"key":"13_CR58","doi-asserted-by":"crossref","unstructured":"Schuler, C.J., Christopher Burger, H., Harmeling, S., Scholkopf, B.: A machine learning approach for non-blind image deconvolution. In: CVPR, pp. 1067\u20131074 (2013)","DOI":"10.1109\/CVPR.2013.142"},{"key":"13_CR59","doi-asserted-by":"crossref","unstructured":"Suin, M., Purohit, K., Rajagopalan, A.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: CVPR, pp. 3606\u20133615 (2020)","DOI":"10.1109\/CVPR42600.2020.00366"},{"key":"13_CR60","doi-asserted-by":"crossref","unstructured":"Sun, J., Cao, W., Xu, Z., Ponce, J.: Learning a convolutional neural network for non-uniform motion blur removal. In: CVPR, pp. 769\u2013777 (2015)","DOI":"10.1109\/CVPR.2015.7298677"},{"key":"13_CR61","unstructured":"Sun, L., Cho, S., Wang, J., Hays, J.: Edge-based blur kernel estimation using patch priors. In: ICCP, pp. 1\u20138 (2013)"},{"key":"13_CR62","doi-asserted-by":"crossref","unstructured":"Tao, X., Gao, H., Shen, X., Wang, J., Jia, J.: Scale-recurrent network for deep image deblurring. In: CVPR, pp. 8174\u20138182 (2018)","DOI":"10.1109\/CVPR.2018.00853"},{"key":"13_CR63","doi-asserted-by":"crossref","unstructured":"Tappen, M.F., Liu, C., Adelson, E.H., Freeman, W.T.: Learning gaussian conditional random fields for low-level vision. In: CVPR, pp. 1\u20138 (2007)","DOI":"10.1109\/CVPR.2007.382979"},{"key":"13_CR64","doi-asserted-by":"crossref","unstructured":"Venkatanath, N., Praneeth, D., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: National Conference on Communications (NCC), pp. 1\u20136 (2015)","DOI":"10.1109\/NCC.2015.7084843"},{"issue":"3","key":"13_CR65","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1137\/080724265","volume":"1","author":"Y Wang","year":"2008","unstructured":"Wang, Y., Yang, J., Yin, W., Zhang, Y.: A new alternating minimization algorithm for total variation image reconstruction. SIAM J. Imag. Sci. 1(3), 248\u2013272 (2008)","journal-title":"SIAM J. Imag. Sci."},{"issue":"4","key":"13_CR66","first-page":"600","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 TIP 13(4), 600\u2013612 (2004)","journal-title":"IEEE TIP"},{"key":"13_CR67","first-page":"1329","volume":"40","author":"J Xiang","year":"2021","unstructured":"Xiang, J., Dong, Y., Yang, Y.: FISTA-net: Learning a fast iterative shrinkage thresholding network for inverse problems in imaging. IEEE TMI 40, 1329\u20131339 (2021)","journal-title":"IEEE TMI"},{"key":"13_CR68","unstructured":"Yang, Y., Sun, J., Li, H., Xu, Z.: Deep ADMM-net for compressive sensing MRI. In: NeurIPS, pp. 10\u201318 (2016)"},{"key":"13_CR69","doi-asserted-by":"crossref","unstructured":"Zhang, J., Ghanem, B.: ISTA-net: interpretable optimization-inspired deep network for image compressive sensing. In: CVPR, pp. 1828\u20131837 (2018)","DOI":"10.1109\/CVPR.2018.00196"},{"key":"13_CR70","doi-asserted-by":"crossref","unstructured":"Zhang, J., shan Pan, J., Lai, W.S., Lau, R.W.H., Yang, M.H.: Learning fully convolutional networks for iterative non-blind deconvolution. In: CVPR, pp. 6969\u20136977 (2017)","DOI":"10.1109\/CVPR.2017.737"},{"key":"13_CR71","doi-asserted-by":"crossref","unstructured":"Zhang, K., Gool, L.V., Timofte, R.: Deep unfolding network for image super-resolution. In: CVPR, pp. 3217\u20133226 (2020)","DOI":"10.1109\/CVPR42600.2020.00328"},{"key":"13_CR72","doi-asserted-by":"crossref","first-page":"6360","DOI":"10.1109\/TPAMI.2021.3088914","volume":"44","author":"K Zhang","year":"2021","unstructured":"Zhang, K., Li, Y., Zuo, W., Zhang, L., Van Gool, L., Timofte, R.: Plug-and-play image restoration with deep denoiser prior. IEEE TPAMI. 44, 6360\u20136376 (2021)","journal-title":"IEEE TPAMI."},{"key":"13_CR73","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Gu, S., Zhang, L.: Learning deep CNN denoiser prior for image restoration. In: CVPR, pp. 3929\u20133938 (2017)","DOI":"10.1109\/CVPR.2017.300"},{"key":"13_CR74","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Zhang, L.: Deep plug-and-play super-resolution for arbitrary blur kernels. In: CVPR, pp. 1671\u20131681 (2019)","DOI":"10.1109\/CVPR.2019.00177"},{"key":"13_CR75","doi-asserted-by":"crossref","unstructured":"Zoran, D., Weiss, Y.: From learning models of natural image patches to whole image restoration. In: ICCV, pp. 479\u2013486 (2011)","DOI":"10.1109\/ICCV.2011.6126278"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19800-7_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T17:52:39Z","timestamp":1728323559000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19800-7_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031197994","9783031198007"],"references-count":75,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19800-7_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"9 November 2022","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":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1645","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.21","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.91","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}