{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T10:24:52Z","timestamp":1760955892222,"version":"3.40.3"},"publisher-location":"Cham","reference-count":47,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030012632"},{"type":"electronic","value":"9783030012649"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"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":[[2018]]},"DOI":"10.1007\/978-3-030-01264-9_35","type":"book-chapter","created":{"date-parts":[[2018,10,8]],"date-time":"2018-10-08T08:20:53Z","timestamp":1538986853000},"page":"591-608","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["Deep Kalman Filtering Network for Video Compression Artifact Reduction"],"prefix":"10.1007","author":[{"given":"Guo","family":"Lu","sequence":"first","affiliation":[]},{"given":"Wanli","family":"Ouyang","sequence":"additional","affiliation":[]},{"given":"Dong","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Xiaoyun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zhiyong","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Ming-Ting","family":"Sun","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,10,9]]},"reference":[{"issue":"12","key":"35_CR1","first-page":"1649","volume":"22","author":"GJ Sullivan","year":"2012","unstructured":"Sullivan, G.J., Ohm, J., Han, W.J., Wiegand, T.: Overview of the high efficiency video coding (HEVC) standard. TCSVT 22(12), 1649\u20131668 (2012)","journal-title":"TCSVT"},{"issue":"9","key":"35_CR2","first-page":"1103","volume":"17","author":"H Schwarz","year":"2007","unstructured":"Schwarz, H., Marpe, D., Wiegand, T.: Overview of the scalable video coding extension of the H. 264\/AVC standard. TCSVT 17(9), 1103\u20131120 (2007)","journal-title":"TCSVT"},{"issue":"2","key":"35_CR3","first-page":"678","volume":"27","author":"G Lu","year":"2018","unstructured":"Lu, G., Zhang, X., Chen, L., Gao, Z.: Novel integration of frame rate up conversion and HEVC coding based on rate-distortion optimization. TIP 27(2), 678\u2013691 (2018)","journal-title":"TIP"},{"issue":"1","key":"35_CR4","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1006\/jvci.1997.0378","volume":"9","author":"MY Shen","year":"1998","unstructured":"Shen, M.Y., Kuo, C.C.J.: Review of postprocessing techniques for compression artifact removal. J. Vis. Commun. Image Represent. 9(1), 2\u201314 (1998)","journal-title":"J. Vis. Commun. Image Represent."},{"key":"35_CR5","doi-asserted-by":"crossref","unstructured":"Reeve, H.C., Lim, J.S.: Reduction of blocking effects in image coding. Opt. Eng. 23(1) (1984)","DOI":"10.1117\/12.7973248"},{"issue":"6","key":"35_CR6","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1016\/j.image.2012.03.002","volume":"27","author":"C Jung","year":"2012","unstructured":"Jung, C., Jiao, L., Qi, H., Sun, T.: Image deblocking via sparse representation. Signal Process. Image Commun. 27(6), 663\u2013677 (2012)","journal-title":"Signal Process. Image Commun."},{"key":"35_CR7","doi-asserted-by":"crossref","unstructured":"Choi, I., Kim, S., Brown, M.S., Tai, Y.W.: A learning-based approach to reduce JPEG artifacts in image matting. In: ICCV (2013)","DOI":"10.1109\/ICCV.2013.358"},{"issue":"3","key":"35_CR8","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1109\/TSP.2013.2290508","volume":"62","author":"H Chang","year":"2014","unstructured":"Chang, H., Ng, M.K., Zeng, T.: Reducing artifacts in JPEG decompression via a learned dictionary. IEEE Trans. Signal Process. 62(3), 718\u2013728 (2014)","journal-title":"IEEE Trans. Signal Process."},{"key":"35_CR9","doi-asserted-by":"crossref","unstructured":"Liu, X., Wu, X., Zhou, J., Zhao, D.: Data-driven sparsity-based restoration of JPEG-compressed images in dual transform-pixel domain. In: CVPR, vol. 1. p. 5 (2015)","DOI":"10.1109\/CVPR.2015.7299153"},{"key":"35_CR10","doi-asserted-by":"crossref","unstructured":"Ouyang, W., Wang, X.: Joint deep learning for pedestrian detection. In: ICCV (2013)","DOI":"10.1109\/ICCV.2013.257"},{"key":"35_CR11","doi-asserted-by":"crossref","unstructured":"Ouyang, W., et al.: Deepid-net: deformable deep convolutional neural networks for object detection. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298854"},{"key":"35_CR12","doi-asserted-by":"crossref","unstructured":"Wang, L., Ouyang, W., Wang, X., Lu, H.: Visual tracking with fully convolutional networks. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.357"},{"key":"35_CR13","doi-asserted-by":"crossref","unstructured":"Zhao, R., Ouyang, W., Wang, X.: Unsupervised salience learning for person re-identification. In: CVPR (2013)","DOI":"10.1109\/CVPR.2013.460"},{"key":"35_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1007\/978-3-319-10593-2_13","volume-title":"Computer Vision \u2013 ECCV 2014","author":"C Dong","year":"2014","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 184\u2013199. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10593-2_13"},{"issue":"2","key":"35_CR15","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":"35_CR16","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., Liu, X., Xu, C.: Memnet: a persistent memory network for image restoration. In: CVPR (2017)","DOI":"10.1109\/ICCV.2017.486"},{"issue":"7","key":"35_CR17","first-page":"3142","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. TIP 26(7), 3142\u20133155 (2017)","journal-title":"TIP"},{"key":"35_CR18","doi-asserted-by":"crossref","unstructured":"Dong, C., Deng, Y., Change Loy, C., Tang, X.: Compression artifacts reduction by a deep convolutional network. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.73"},{"key":"35_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1007\/978-3-319-46448-0_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J Guo","year":"2016","unstructured":"Guo, J., Chao, H.: Building dual-domain representations for compression artifacts reduction. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 628\u2013644. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_38"},{"key":"35_CR20","doi-asserted-by":"crossref","unstructured":"Galteri, L., Seidenari, L., Bertini, M., Del Bimbo, A.: Deep generative adversarial compression artifact removal. arXiv preprint arXiv:1704.02518 (2017)","DOI":"10.1109\/ICCV.2017.517"},{"key":"35_CR21","unstructured":"Xue, T., Chen, B., Wu, J., Wei, D., Freeman, W.T.: Video enhancement with task-oriented flow. arXiv preprint arXiv:1711.09078 (2017)"},{"key":"35_CR22","doi-asserted-by":"crossref","unstructured":"Tao, X., Gao, H., Liao, R., Wang, J., Jia, J.: Detail-revealing deep video super-resolution. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.479"},{"key":"35_CR23","doi-asserted-by":"crossref","unstructured":"Liu, D., et al.: Robust video super-resolution with learned temporal dynamics. In: CVPR (2017)","DOI":"10.1109\/ICCV.2017.274"},{"key":"35_CR24","doi-asserted-by":"crossref","unstructured":"Caballero, J., et al.: Real-time video super-resolution with spatio-temporal networks and motion compensation. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.304"},{"issue":"5","key":"35_CR25","first-page":"1395","volume":"16","author":"A Foi","year":"2007","unstructured":"Foi, A., Katkovnik, V., Egiazarian, K.: Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color images. TIP 16(5), 1395\u20131411 (2007)","journal-title":"TIP"},{"issue":"12","key":"35_CR26","first-page":"4613","volume":"22","author":"X Zhang","year":"2013","unstructured":"Zhang, X., Xiong, R., Fan, X., Ma, S., Gao, W.: Compression artifact reduction by overlapped-block transform coefficient estimation with block similarity. TIP 22(12), 4613\u20134626 (2013)","journal-title":"TIP"},{"key":"35_CR27","doi-asserted-by":"crossref","unstructured":"Wang, Z., Liu, D., Chang, S., Ling, Q., Yang, Y., Huang, T.S.: D3: dep dual-domain based fast restoration of JPEG-compressed images. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.302"},{"key":"35_CR28","unstructured":"Svoboda, P., Hradis, M., Barina, D., Zemcik, P.: Compression artifacts removal using convolutional neural networks. arXiv preprint arXiv:1605.00366 (2016)"},{"key":"35_CR29","unstructured":"Mao, X.J., Shen, C., Yang, Y.B.: Image denoising using very deep fully convolutional encoder-decoder networks with symmetric skip connections. arXiv preprint (2016)"},{"key":"35_CR30","doi-asserted-by":"crossref","unstructured":"Guo, J., Chao, H.: One-to-many network for visually pleasing compression artifacts reduction. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.517"},{"key":"35_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Gu, S., Zhang, L.: Learning deep CNN denoiser prior for image restoration. arXiv preprint (2017)","DOI":"10.1109\/CVPR.2017.300"},{"key":"35_CR32","doi-asserted-by":"crossref","unstructured":"Chang, J.R., Li, C.L., Poczos, B., Kumar, B.V., Sankaranarayanan, A.C.: One network to solve them allsolving linear inverse problems using deep projection models. arXiv preprint (2017)","DOI":"10.1109\/ICCV.2017.627"},{"key":"35_CR33","unstructured":"Bigdeli, S.A., Zwicker, M., Favaro, P., Jin, M.: Deep mean-shift priors for image restoration. In: NIPS (2017)"},{"key":"35_CR34","doi-asserted-by":"crossref","unstructured":"Liao, R., Tao, X., Li, R., Ma, Z., Jia, J.: Video super-resolution via deep draft-ensemble learning. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.68"},{"issue":"2","key":"35_CR35","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1109\/TCI.2016.2532323","volume":"2","author":"A Kappeler","year":"2016","unstructured":"Kappeler, A., Yoo, S., Dai, Q., Katsaggelos, A.K.: Video super-resolution with convolutional neural networks. IEEE Trans. Comput. Imaging 2(2), 109\u2013122 (2016)","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"35_CR36","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: NIPS (2015)"},{"key":"35_CR37","unstructured":"Shashua, S.D.C., Mannor, S.: Deep robust kalman filter. arXiv preprint arXiv:1703.02310 (2017)"},{"key":"35_CR38","unstructured":"Krishnan, R.G., Shalit, U., Sontag, D.: Deep Kalman filters. arXiv preprint arXiv:1511.05121 (2015)"},{"issue":"1","key":"35_CR39","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1115\/1.3662552","volume":"82","author":"RE Kalman","year":"1960","unstructured":"Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35\u201345 (1960)","journal-title":"J. Basic Eng."},{"key":"35_CR40","doi-asserted-by":"publisher","DOI":"10.1002\/0471221546","volume-title":"Kalman Filtering and Neural Networks","author":"SS Haykin","year":"2001","unstructured":"Haykin, S.S.: Kalman Filtering and Neural Networks. Wiley Online Library, New York (2001)"},{"key":"35_CR41","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/978-3-319-46493-0_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630\u2013645. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_38"},{"key":"35_CR42","unstructured":"Ball\u00e9, J., Laparra, V., Simoncelli, E.P.: Density modeling of images using a generalized normalization transformation. arXiv preprint arXiv:1511.06281 (2015)"},{"key":"35_CR43","unstructured":"Abadi, M., et al.: Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016)"},{"issue":"4","key":"35_CR44","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. TIP 13(4), 600\u2013612 (2004)","journal-title":"TIP"},{"key":"35_CR45","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"35_CR46","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: International Conference on Artificial Intelligence and Statistics (2010)"},{"issue":"9","key":"35_CR47","first-page":"3952","volume":"21","author":"M Maggioni","year":"2012","unstructured":"Maggioni, M., Boracchi, G., Foi, A., Egiazarian, K.: Video denoising, deblocking, and enhancement through separable 4-d nonlocal spatiotemporal transforms. TIP 21(9), 3952\u20133966 (2012)","journal-title":"TIP"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2018"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-01264-9_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T00:56:05Z","timestamp":1665190565000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-01264-9_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030012632","9783030012649"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01264-9_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"9 October 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}