{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:53:56Z","timestamp":1776275636915,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":50,"publisher":"Springer Singapore","isbn-type":[{"value":"9789811358401","type":"print"},{"value":"9789811358418","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-981-13-5841-8_59","type":"book-chapter","created":{"date-parts":[[2019,5,11]],"date-time":"2019-05-11T21:25:16Z","timestamp":1557609916000},"page":"563-572","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Deep Learning for Image Denoising: A Survey"],"prefix":"10.1007","author":[{"given":"Chunwei","family":"Tian","sequence":"first","affiliation":[]},{"given":"Yong","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Lunke","family":"Fei","sequence":"additional","affiliation":[]},{"given":"Ke","family":"Yan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,5,12]]},"reference":[{"key":"59_CR1","unstructured":"Ahn, B., Cho, N.I.: Block-matching convolutional neural network for image denoising (2017). arXiv:1704.00524"},{"key":"59_CR2","unstructured":"Arora, S., Bhaskara, A., Ge, R., Ma, T.: Provable bounds for learning some deep representations. In: International Conference on Machine Learning, pp. 584\u2013592 (2014)"},{"issue":"4","key":"59_CR3","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1145\/3072959.3073708","volume":"36","author":"S Bako","year":"2017","unstructured":"Bako, S., Vogels, T., McWilliams, B., Meyer, M., Nov\u00e1k, J., Harvill, A., Sen, P., Derose, T., Rousselle, F.: Kernel-predicting convolutional networks for denoising monte carlo renderings. ACM Trans. Graph 36(4), 97 (2017)","journal-title":"ACM Trans. Graph"},{"issue":"2","key":"59_CR4","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/72.279181","volume":"5","author":"Y Bengio","year":"1994","unstructured":"Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157\u2013166 (1994)","journal-title":"IEEE Trans. Neural Netw."},{"key":"59_CR5","unstructured":"Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: CVPR 2005. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, vol.\u00a02, pp. 60\u201365. IEEE (2005)"},{"issue":"2","key":"59_CR6","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/s11263-007-0052-1","volume":"76","author":"A Buades","year":"2008","unstructured":"Buades, A., Coll, B., Morel, J.-M.: Nonlocal image and movie denoising. Int. J. Comput. Vis. 76(2), 123\u2013139 (2008)","journal-title":"Int. J. Comput. Vis."},{"issue":"6","key":"59_CR7","doi-asserted-by":"publisher","first-page":"1256","DOI":"10.1109\/TPAMI.2016.2596743","volume":"39","author":"Y Chen","year":"2017","unstructured":"Chen, Y., Pock, T.: Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1256\u20131272 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"59_CR8","unstructured":"Choi, J.H., Elgendy, O., Chan, S.H.: Integrating disparate sources of experts for robust image denoising (2017). arXiv:1711.06712"},{"issue":"8","key":"59_CR9","doi-asserted-by":"publisher","first-page":"2080","DOI":"10.1109\/TIP.2007.901238","volume":"16","author":"K Dabov","year":"2007","unstructured":"Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080\u20132095 (2007)","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"59_CR10","doi-asserted-by":"publisher","first-page":"1620","DOI":"10.1109\/TIP.2012.2235847","volume":"22","author":"W Dong","year":"2013","unstructured":"Dong, W., Zhang, L., Shi, G., Li, X.: Nonlocally centralized sparse representation for image restoration. IEEE Trans. Image Process. 22(4), 1620\u20131630 (2013)","journal-title":"IEEE Trans. Image Process."},{"key":"59_CR11","doi-asserted-by":"crossref","unstructured":"Esser, P., Sutter, E., Ommer, B.: A variational u-net for conditional appearance and shape generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8857\u20138866 (2018)","DOI":"10.1109\/CVPR.2018.00923"},{"key":"59_CR12","doi-asserted-by":"crossref","unstructured":"Fei, L., Lu, G., Jia, W., Teng, S., Zhang, D.: Feature extraction methods for palmprint recognition: a survey and evaluation. IEEE Trans. Syst. Man Cybern.: Syst. (2018)","DOI":"10.1109\/TSMC.2018.2795609"},{"key":"59_CR13","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"59_CR14","doi-asserted-by":"crossref","unstructured":"Gu, S., Zhang, L., Zuo, W., Feng, X.: Weighted nuclear norm minimization with application to image denoising. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2862\u20132869 (2014)","DOI":"10.1109\/CVPR.2014.366"},{"key":"59_CR15","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"59_CR16","doi-asserted-by":"crossref","unstructured":"Hou, Q., Cheng, M.-M., Hu, X., Borji, A., Tu, Z., Torr, P.: Deeply supervised salient object detection with short connections. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5300\u20135309. IEEE (2017)","DOI":"10.1109\/CVPR.2017.563"},{"key":"59_CR17","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift (2015). arXiv:1502.03167"},{"key":"59_CR18","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"key":"59_CR19","doi-asserted-by":"crossref","unstructured":"Lan, X., Roth, S., Huttenlocher, D., Black, M.J.: Efficient belief propagation with learned higher-order Markov random fields. In: European Conference on Computer Vision, pp. 269\u2013282. Springer (2006)","DOI":"10.1007\/11744047_21"},{"issue":"1","key":"59_CR20","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/72.554195","volume":"8","author":"S. Lawrence","year":"1997","unstructured":"Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D.: Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8(1), 98\u2013113 (1997)","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"4","key":"59_CR21","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y. LeCun","year":"1989","unstructured":"LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541\u2013551 (1989)","journal-title":"Neural Computation"},{"key":"59_CR22","doi-asserted-by":"crossref","unstructured":"Lefkimmiatis, S.: Universal denoising networks: a novel CNN architecture for image denoising. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3204\u20133213 (2018)","DOI":"10.1109\/CVPR.2018.00338"},{"key":"59_CR23","doi-asserted-by":"crossref","unstructured":"Li, S., Li, W., Cook, C., Zhu, C., Gao, Y.: Independently recurrent neural network (indrnn): Building a longer and deeper RNN. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5457\u20135466 (2018)","DOI":"10.1109\/CVPR.2018.00572"},{"key":"59_CR24","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"Geert Litjens","year":"2017","unstructured":"Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., van\u00a0der Laak, J.A.W.M., Van\u00a0Ginneken, B., S\u00e1nchez, C.I.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017)","journal-title":"Medical Image Analysis"},{"key":"59_CR25","unstructured":"Qin, Y., Tian, C.: Weighted feature space representation with kernel for image classification. Arab. J. Sci. Eng. 1\u201313 (2017)"},{"key":"59_CR26","doi-asserted-by":"crossref","unstructured":"Schmidt, U., Roth, S.: Shrinkage fields for effective image restoration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2774\u20132781 (2014)","DOI":"10.1109\/CVPR.2014.349"},{"key":"59_CR27","unstructured":"Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: integrated recognition, localization and detection using convolutional networks (2013). arXiv:1312.6229"},{"issue":"8","key":"59_CR28","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1109\/34.868688","volume":"22","author":"J Shi","year":"2000","unstructured":"Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888\u2013905 (2000)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"59_CR29","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv:1409.1556"},{"key":"59_CR30","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"59_CR31","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., Liu, X., Xu, C.: Memnet: a persistent memory network for image restoration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4539\u20134547 (2017)","DOI":"10.1109\/ICCV.2017.486"},{"issue":"2","key":"59_CR32","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1007\/s13369-017-2696-7","volume":"43","author":"C Tian","year":"2018","unstructured":"Tian, C., Zhang, Q., Sun, G., Song, Z., Li, S.: Fft consolidated sparse and collaborative representation for image classification. Arab. J. Sci. Eng. 43(2), 741\u2013758 (2018)","journal-title":"Arab. J. Sci. Eng."},{"key":"59_CR33","unstructured":"Tripathi, S., Lipton, Z.C., Nguyen, T.Q.: Correction by projection: denoising images with generative adversarial networks (2018). arXiv:1803.04477"},{"key":"59_CR34","doi-asserted-by":"crossref","unstructured":"Wang, H., Kl\u00e4ser, A., Schmid, C., Liu, C.-L.: Action recognition by dense trajectories. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3169\u20133176. IEEE (2011)","DOI":"10.1109\/CVPR.2011.5995407"},{"issue":"4","key":"59_CR35","doi-asserted-by":"publisher","first-page":"1424","DOI":"10.1109\/TIP.2015.2403231","volume":"24","author":"Li Wang","year":"2015","unstructured":"Wang, L., Liu, T., Wang, G., Chan, K.L., Yang, Q.: Video tracking using learned hierarchical features. IEEE Trans. Image Process. 24(4), 1424\u20131435 (2015)","journal-title":"IEEE Transactions on Image Processing"},{"key":"59_CR36","unstructured":"Wang, T., Sun, M., Hu, K.: Dilated residual network for image denoising (2017). arXiv:1708.05473"},{"key":"59_CR37","doi-asserted-by":"crossref","unstructured":"Wang, G., Wang, G., Pan, Z., Zhang, Z.: Multiplicative noise removal using deep CNN denoiser prior. In: 2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), pp. 1\u20136. IEEE (2017)","DOI":"10.1109\/ISPACS.2017.8265635"},{"key":"59_CR38","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.neunet.2018.08.007","volume":"108","author":"J Wen","year":"2018","unstructured":"Wen, J., Fang, X., Yong, X., Tian, C., Fei, L.: Low-rank representation with adaptive graph regularization. Neural Netw. 108, 83\u201396 (2018)","journal-title":"Neural Netw."},{"key":"59_CR39","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01261-8_1","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Yuxin Wu","year":"2018","unstructured":"Wu, Y., He, K.: Group normalization (2018). arXiv:1803.08494"},{"key":"59_CR40","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5987\u20135995. IEEE (2017)","DOI":"10.1109\/CVPR.2017.634"},{"key":"59_CR41","doi-asserted-by":"crossref","unstructured":"Xie, W., Li, Y., Jia, X.: Deep convolutional networks with residual learning for accurate spectral-spatial denoising. Neurocomputing (2018)","DOI":"10.1016\/j.neucom.2018.05.115"},{"key":"59_CR42","doi-asserted-by":"crossref","unstructured":"Xu, J., Zhang, L., Zuo, W., Zhang, D., Feng, X.: Patch group based nonlocal self-similarity prior learning for image denoising. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 244\u2013252 (2015)","DOI":"10.1109\/ICCV.2015.36"},{"key":"59_CR43","doi-asserted-by":"crossref","unstructured":"You, Y., Zhang, Z., Hsieh, C.-J., Demmel, J., Keutzer, K.: 100-epoch imagenet training with alexnet in 24 minutes (2017)","DOI":"10.1145\/3225058.3225069"},{"key":"59_CR44","unstructured":"Zagoruyko, S., Komodakis, N.: Diracnets: training very deep neural networks without skip-connections (2017). arXiv:1706.00388"},{"issue":"7","key":"59_CR45","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":"5","key":"59_CR46","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1109\/MSP.2017.2717489","volume":"34","author":"L Zhang","year":"2017","unstructured":"Zhang, L., Zuo, W.: Image restoration: from sparse and low-rank priors to deep priors. IEEE Signal Process. Mag. 34(5), 172\u2013179 (2017)","journal-title":"IEEE Signal Process. Mag."},{"key":"59_CR47","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Zhang, L.: FFDNet: toward a fast and flexible solution for CNN based image denoising. IEEE Trans. Image Process. (2018)","DOI":"10.1109\/TIP.2018.2839891"},{"key":"59_CR48","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Zhang, L.: Learning a single convolutional super-resolution network for multiple degradations. In: IEEE Conference on Computer Vision and Pattern Recognition, vol.\u00a06 (2018)","DOI":"10.1109\/CVPR.2018.00344"},{"key":"59_CR49","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00262"},{"key":"59_CR50","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Wu, W., Zou, W., Yan, J.: End-to-end flow correlation tracking with spatial-temporal attention. Illumination 42, 20 (2017)","DOI":"10.1109\/CVPR.2018.00064"}],"container-title":["Advances in Intelligent Systems and Computing","Genetic and Evolutionary Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-13-5841-8_59","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,12]],"date-time":"2020-12-12T12:44:05Z","timestamp":1607777045000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-13-5841-8_59"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9789811358401","9789811358418"],"references-count":50,"URL":"https:\/\/doi.org\/10.1007\/978-981-13-5841-8_59","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"value":"2194-5357","type":"print"},{"value":"2194-5365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"12 May 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICGEC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Genetic and Evolutionary Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changzhou","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":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 December 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 December 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icgec2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}