{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:40:25Z","timestamp":1778168425097,"version":"3.51.4"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T00:00:00Z","timestamp":1718668800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T00:00:00Z","timestamp":1718668800000},"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":["Machine Vision and Applications"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s00138-024-01563-x","type":"journal-article","created":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T20:09:48Z","timestamp":1718741388000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multi-scale information fusion generative adversarial network for real-world noisy image denoising"],"prefix":"10.1007","volume":"35","author":[{"given":"Xuegang","family":"Hu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,18]]},"reference":[{"key":"1563_CR1","first-page":"1","volume":"71","author":"N Zeng","year":"2022","unstructured":"Zeng, N., Wu, P., Wang, Z., Li, H., Liu, W., Liu, X.: A small-sized object detection oriented multi-scale feature fusion approach with application to defect detection. IEEE Trans. Instrum. Meas. 71, 1\u201314 (2022)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"1563_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108873","volume":"131","author":"X Ning","year":"2022","unstructured":"Ning, X., Tian, W., Yu, Z., Li, W., Bai, X., Wang, Y.: Hcfnn: high-order coverage function neural network for image classification. Pattern Recognit. 131, 108873 (2022)","journal-title":"Pattern Recognit."},{"key":"1563_CR3","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1007\/s00371-021-02075-9","volume":"38","author":"Z Cheng","year":"2022","unstructured":"Cheng, Z., Qu, A., He, X.: Contour-aware semantic segmentation network with spatial attention mechanism for medical image. The Vis. Comput. 38, 749\u2013762 (2022)","journal-title":"The Vis. Comput."},{"key":"1563_CR4","doi-asserted-by":"crossref","unstructured":"Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), vol. 2, pp. 60\u201365 (2005)","DOI":"10.1109\/CVPR.2005.38"},{"issue":"8","key":"1563_CR5","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":"11","key":"1563_CR6","doi-asserted-by":"publisher","first-page":"4311","DOI":"10.1109\/TSP.2006.881199","volume":"54","author":"M Aharon","year":"2006","unstructured":"Aharon, M., Elad, M., Bruckstein, A.: K-svd: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311\u20134322 (2006)","journal-title":"IEEE Trans. Signal Process."},{"key":"1563_CR7","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"},{"issue":"4","key":"1563_CR8","doi-asserted-by":"publisher","first-page":"1994","DOI":"10.1109\/TCSVT.2022.3216681","volume":"33","author":"Y Pan","year":"2022","unstructured":"Pan, Y., Ren, C., Wu, X., Huang, J., He, X.: Real image denoising via guided residual estimation and noise correction. IEEE Trans. Circuits Syst. Video Technol. 33(4), 1994\u20132000 (2022)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1563_CR9","unstructured":"Jain, V., Seung, S.: Natural image denoising with convolutional networks. Adv. Neural Inf. Process. Syst. 21 (2008)"},{"issue":"7","key":"1563_CR10","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":"1563_CR11","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":"1563_CR12","doi-asserted-by":"crossref","unstructured":"Guo, S., Yan, Z., Zhang, K., Zuo, W., Zhang, L.: Toward convolutional blind denoising of real photographs. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1712\u20131722 (2019)","DOI":"10.1109\/CVPR.2019.00181"},{"key":"1563_CR13","doi-asserted-by":"crossref","unstructured":"Anwar, S., Barnes, N.: Real image denoising with feature attention. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3155\u20133164 (2019)","DOI":"10.1109\/ICCV.2019.00325"},{"key":"1563_CR14","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.-H., Shao, L.: Learning enriched features for real image restoration and enhancement. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXV 16, pp. 492\u2013511 (2020)","DOI":"10.1007\/978-3-030-58595-2_30"},{"key":"1563_CR15","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.-H., Shao, L.: Multi-stage progressive image restoration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14821\u201314831 (2021)","DOI":"10.1109\/CVPR46437.2021.01458"},{"issue":"8","key":"1563_CR16","doi-asserted-by":"publisher","first-page":"5124","DOI":"10.1109\/TCSVT.2022.3149518","volume":"32","author":"B Jiang","year":"2022","unstructured":"Jiang, B., Lu, Y., Wang, J., Lu, G., Zhang, D.: Deep image denoising with adaptive priors. IEEE Trans. Circuits Syst. Video Technol. 32(8), 5124\u20135136 (2022)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"4","key":"1563_CR17","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.1007\/s13042-022-01694-5","volume":"14","author":"L Zhou","year":"2023","unstructured":"Zhou, L., Zhou, D., Yang, H., Yang, S.: Multi-scale network toward real-world image denoising. Int. J. Mach. Learn. Cybern. 14(4), 1205\u20131216 (2023)","journal-title":"Int. J. Mach. Learn. Cybern."},{"issue":"2","key":"1563_CR18","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1007\/s11063-022-10934-2","volume":"55","author":"X Jia","year":"2023","unstructured":"Jia, X., Peng, Y., Ge, B., Li, J., Liu, S., Wang, W.: A multi-scale dilated residual convolution network for image denoising. Neural Process. Lett. 55(2), 1231\u20131246 (2023)","journal-title":"Neural Process. Lett."},{"key":"1563_CR19","doi-asserted-by":"crossref","unstructured":"Zhou, L., Zhou, D., Yang, H., Yang, S.: Two-subnet network for real-world image denoising. Multimed. Tools Appl., 1\u201317 (2023)","DOI":"10.1007\/s11042-023-16153-8"},{"key":"1563_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.111320","volume":"284","author":"Y Zuo","year":"2024","unstructured":"Zuo, Y., Yao, W., Zeng, Y., Xie, J., Fang, Y., Huang, Y., Jiang, W.: Cfnet: conditional filter learning with dynamic noise estimation for real image denoising. Knowl.-Based Syst. 284, 111320 (2024)","journal-title":"Knowl.-Based Syst."},{"key":"1563_CR21","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27 (2014)"},{"key":"1563_CR22","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1016\/j.neucom.2022.07.084","volume":"506","author":"J Zhao","year":"2022","unstructured":"Zhao, J., Lee, F., Hu, C., Yu, H., Chen, Q.: Lda-gan: lightweight domain-attention gan for unpaired image-to-image translation. Neurocomputing 506, 355\u2013368 (2022)","journal-title":"Neurocomputing"},{"key":"1563_CR23","doi-asserted-by":"crossref","unstructured":"Chen, Y., Xia, R., Yang, K., Zou, K.: Gcam: lightweight image inpainting via group convolution and attention mechanism. Int. J. Mach. Learn. Cybern. 1\u201311 (2023)","DOI":"10.1007\/s13042-023-01999-z"},{"issue":"6","key":"1563_CR24","volume":"35","author":"Y Chen","year":"2023","unstructured":"Chen, Y., Xia, R., Yang, K., Zou, K.: Dargs: image inpainting algorithm via deep attention residuals group and semantics. J. King Saud Univ.-Comput. Inf. Sci. 35(6), 101567 (2023)","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"1563_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2023.103883","volume":"238","author":"Y Chen","year":"2024","unstructured":"Chen, Y., Xia, R., Yang, K., Zou, K.: Mfmam: image inpainting via multi-scale feature module with attention module. Comput. Vis. Image Underst. 238, 103883 (2024)","journal-title":"Comput. Vis. Image Underst."},{"key":"1563_CR26","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning, pp. 214\u2013223 (2017)"},{"key":"1563_CR27","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of wasserstein gans. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"1563_CR28","doi-asserted-by":"crossref","unstructured":"Chen, J., Chen, J., Chao, H., Yang, M.: Image blind denoising with generative adversarial network based noise modeling. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3155\u20133164 (2018)","DOI":"10.1109\/CVPR.2018.00333"},{"key":"1563_CR29","doi-asserted-by":"crossref","unstructured":"Lin, K., Li, T.H., Liu, S., Li, G.: Real photographs denoising with noise domain adaptation and attentive generative adversarial network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 1717\u20131721 (2019)","DOI":"10.1109\/CVPRW.2019.00221"},{"key":"1563_CR30","doi-asserted-by":"crossref","unstructured":"Zhu, S., Xu, G., Cheng, Y., Han, X., Wang, Z.: Bdgan: Image blind denoising using generative adversarial networks. In: Pattern Recognition and Computer Vision: Second Chinese Conference, PRCV 2019, Xi\u2019an, China, November 8\u201311, 2019, Proceedings, Part II 2, pp. 241\u2013252 (2019)","DOI":"10.1007\/978-3-030-31723-2_21"},{"key":"1563_CR31","doi-asserted-by":"crossref","unstructured":"Kim, D.-W., Ryun Chung, J., Jung, S.-W.: Grdn: Grouped residual dense network for real image denoising and gan-based real-world noise modeling. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 2086\u20132094 (2019)","DOI":"10.1109\/CVPRW.2019.00261"},{"key":"1563_CR32","doi-asserted-by":"crossref","unstructured":"Yue, Z., Zhao, Q., Zhang, L., Meng, D.: Dual adversarial network: Toward real-world noise removal and noise generation. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part X 16, pp. 41\u201358 (2020)","DOI":"10.1007\/978-3-030-58607-2_3"},{"key":"1563_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106478","volume":"95","author":"Q Lyu","year":"2020","unstructured":"Lyu, Q., Guo, M., Pei, Z.: Degan: mixed noise removal via generative adversarial networks. Appl. Soft Comput. 95, 106478 (2020)","journal-title":"Appl. Soft Comput."},{"key":"1563_CR34","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.ins.2021.04.045","volume":"570","author":"DM Vo","year":"2021","unstructured":"Vo, D.M., Nguyen, D.M., Le, T.P., Lee, S.-W.: Hi-gan: a hierarchical generative adversarial network for blind denoising of real photographs. Inf. Sci. 570, 225\u2013240 (2021)","journal-title":"Inf. Sci."},{"issue":"1","key":"1563_CR35","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1109\/JSEN.2023.3312389","volume":"24","author":"S Zhao","year":"2024","unstructured":"Zhao, S., Lin, S., Cheng, X., Zhou, K., Zhang, M., Wang, H.: Dual-gan complementary learning for real-world image denoising. IEEE Sens. J. 24(1), 355\u2013366 (2024)","journal-title":"IEEE Sens. J."},{"key":"1563_CR36","doi-asserted-by":"publisher","first-page":"2124","DOI":"10.1109\/LSP.2020.3039726","volume":"27","author":"Y Song","year":"2020","unstructured":"Song, Y., Zhu, Y., Du, X.: Grouped multi-scale network for real-world image denoising. IEEE Signal Process. Lett. 27, 2124\u20132128 (2020)","journal-title":"IEEE Signal Process. Lett."},{"key":"1563_CR37","doi-asserted-by":"publisher","first-page":"19945","DOI":"10.1007\/s11042-019-7377-y","volume":"78","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Wang, G., Chen, C., Pan, Z.: Multi-scale dilated convolution of convolutional neural network for image denoising. Multimed. Tools Appl. 78, 19945\u201319960 (2019)","journal-title":"Multimed. Tools Appl."},{"key":"1563_CR38","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1007\/s11760-021-01984-5","volume":"16","author":"X Yu","year":"2022","unstructured":"Yu, X., Fu, Z., Ge, C.: A multi-scale generative adversarial network for real-world image denoising. Signal Image Video Process. 16, 257\u2013264 (2022)","journal-title":"Signal Image Video Process."},{"key":"1563_CR39","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wang, L., Duan, S., Li, Y.: An image denoising method based on deep residual gan. In: Journal of Physics: Conference Series, vol. 1550, p. 032127 (2020)","DOI":"10.1088\/1742-6596\/1550\/3\/032127"},{"key":"1563_CR40","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":"1563_CR41","doi-asserted-by":"crossref","unstructured":"Wu, W., Lv, G., Duan, Y., Liang, P., Zhang, Y., Xia, Y.: Dcanet: Dual convolutional neural network with attention for image blind denoising. arXiv preprint arXiv:2304.01498 (2023)","DOI":"10.1007\/s00530-024-01469-8"},{"key":"1563_CR42","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125\u20131134 (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"1563_CR43","doi-asserted-by":"crossref","unstructured":"Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II 14, pp. 694\u2013711 (2016)","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"1563_CR44","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","volume":"60","author":"L Rubin","year":"1992","unstructured":"Rubin, L.: Nonlinenr total variation based noise removal algorithms. Phys. D: Nonlinear Phenom. 60, 259\u2013265 (1992)","journal-title":"Phys. D: Nonlinear Phenom."},{"key":"1563_CR45","doi-asserted-by":"crossref","unstructured":"Li, R., Pan, J., Li, Z., Tang, J.: Single image dehazing via conditional generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8202\u20138211 (2018)","DOI":"10.1109\/CVPR.2018.00856"},{"key":"1563_CR46","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1563_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.123111","volume":"245","author":"Y Chen","year":"2024","unstructured":"Chen, Y., Xia, R., Yang, K., Zou, K.: Micu: image super-resolution via multi-level information compensation and u-net. Expert Syst. Appl. 245, 123111 (2024)","journal-title":"Expert Syst. Appl."},{"key":"1563_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.111392","volume":"154","author":"Y Chen","year":"2024","unstructured":"Chen, Y., Xia, R., Yang, K., Zou, K.: Dnnam: image inpainting algorithm via deep neural networks and attention mechanism. Appl. Soft Comput. 154, 111392 (2024)","journal-title":"Appl. Soft Comput."},{"issue":"10","key":"1563_CR49","doi-asserted-by":"publisher","first-page":"3","DOI":"10.23915\/distill.00003","volume":"1","author":"A Odena","year":"2016","unstructured":"Odena, A., Dumoulin, V., Olah, C.: Deconvolution and checkerboard artifacts. Distill 1(10), 3 (2016)","journal-title":"Distill"},{"key":"1563_CR50","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part IV 14, pp. 630\u2013645 (2016)","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"1563_CR51","doi-asserted-by":"crossref","unstructured":"Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 624\u2013632 (2017)","DOI":"10.1109\/CVPR.2017.618"},{"key":"1563_CR52","doi-asserted-by":"crossref","unstructured":"Seif, G., Androutsos, D.: Edge-based loss function for single image super-resolution. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1468\u20131472 (2018)","DOI":"10.1109\/ICASSP.2018.8461664"},{"key":"1563_CR53","doi-asserted-by":"crossref","unstructured":"Jiang, K., Wang, Z., Yi, P., Chen, C., Huang, B., Luo, Y., Ma, J., Jiang, J.: Multi-scale progressive fusion network for single image deraining. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8346\u20138355 (2020)","DOI":"10.1109\/CVPR42600.2020.00837"},{"issue":"10","key":"1563_CR54","doi-asserted-by":"publisher","first-page":"1467","DOI":"10.1109\/83.791975","volume":"8","author":"B Kamgar-Parsi","year":"1999","unstructured":"Kamgar-Parsi, B., Rosenfeld, A.: Optimally isotropic laplacian operator. IEEE Trans. Image Process. 8(10), 1467\u20131472 (1999)","journal-title":"IEEE Trans. Image Process."},{"key":"1563_CR55","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"1563_CR56","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"1563_CR57","doi-asserted-by":"crossref","unstructured":"Abdelhamed, A., Lin, S., Brown, M.S.: A high-quality denoising dataset for smartphone cameras. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1692\u20131700 (2018)","DOI":"10.1109\/CVPR.2018.00182"},{"key":"1563_CR58","doi-asserted-by":"crossref","unstructured":"Plotz, T., Roth, S.: Benchmarking denoising algorithms with real photographs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1586\u20131595 (2017)","DOI":"10.1109\/CVPR.2017.294"},{"key":"1563_CR59","unstructured":"Xu, J., Li, H., Liang, Z., Zhang, D., Zhang, L.: Real-world noisy image denoising: A new benchmark. arXiv preprint arXiv:1804.02603 (2018)"},{"issue":"4","key":"1563_CR60","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","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 Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-024-01563-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-024-01563-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-024-01563-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T03:57:11Z","timestamp":1732247831000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-024-01563-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,18]]},"references-count":60,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["1563"],"URL":"https:\/\/doi.org\/10.1007\/s00138-024-01563-x","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"value":"0932-8092","type":"print"},{"value":"1432-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,18]]},"assertion":[{"value":"29 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 May 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 June 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"81"}}