{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T16:30:57Z","timestamp":1772641857052,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,6,24]],"date-time":"2023-06-24T00:00:00Z","timestamp":1687564800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,24]],"date-time":"2023-06-24T00:00:00Z","timestamp":1687564800000},"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":["SIViP"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1007\/s11760-023-02628-6","type":"journal-article","created":{"date-parts":[[2023,6,24]],"date-time":"2023-06-24T11:46:34Z","timestamp":1687607194000},"page":"3983-3991","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An optimized profound memory-affiliated de-noising of aerial images through deep neural network for disaster management"],"prefix":"10.1007","volume":"17","author":[{"given":"T. Ajith Bosco","family":"Raj","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C.","family":"Pushpalatha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A.","family":"Ahilan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,24]]},"reference":[{"key":"2628_CR1","doi-asserted-by":"crossref","unstructured":"Asokan, A., Anitha, J.: Edge preserved satellite image denoising using median and bilateral filtering. In: International Conference on Recent Trends in Image Processing and Pattern Recognition, pp. 688\u2013699. Springer, Singapore (2018)","DOI":"10.1007\/978-981-13-9181-1_59"},{"issue":"6","key":"2628_CR2","doi-asserted-by":"publisher","first-page":"1520","DOI":"10.1109\/TGRS.2007.895830","volume":"45","author":"S Voigt","year":"2007","unstructured":"Voigt, S., Kemper, T., Riedlinger, T., Kiefl, R., Scholte, K., Mehl, H.: Satellite image analysis for disaster and crisis-management support. IEEE Trans. Geosci. Remote Sens. 45(6), 1520\u20131528 (2007)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"12","key":"2628_CR3","first-page":"2018","volume":"10","author":"W Xu","year":"1893","unstructured":"Xu, W., Xu, G., Wang, Y., Sun, X., Lin, D., Wu, Y.: Deep memory connected neural network for optical remote sensing image restoration. Remote Sens. 10(12), 2018 (1893)","journal-title":"Remote Sens."},{"issue":"3","key":"2628_CR4","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1007\/s41095-020-0176-6","volume":"6","author":"X Wu","year":"2020","unstructured":"Wu, X., Zhou, B., Ren, Q., Guo, W.: Multispectral image denoising using sparse and graph Laplacian Tucker decomposition. Comput. Visual Media 6(3), 319\u2013331 (2020)","journal-title":"Comput. Visual Media"},{"issue":"3","key":"2628_CR5","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1109\/TGRS.2010.2075937","volume":"49","author":"G Chen","year":"2010","unstructured":"Chen, G., Qian, S.E.: Denoising of hyperspectral imagery using principal component analysis and wavelet shrinkage. IEEE Trans. Geosci. Remote Sens. 49(3), 973\u2013980 (2010)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"7","key":"2628_CR6","doi-asserted-by":"publisher","first-page":"1399","DOI":"10.1007\/s11760-012-0369-2","volume":"8","author":"TS Sharmila","year":"2014","unstructured":"Sharmila, T.S., Ramar, K.: Efficient analysis of hybrid directional lifting technique for satellite image denoising. Signal Image Video Process. 8(7), 1399\u20131404 (2014)","journal-title":"Signal Image Video Process."},{"issue":"3","key":"2628_CR7","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/s11220-013-0075-3","volume":"13","author":"AM Ragheb","year":"2012","unstructured":"Ragheb, A.M., Osman, H., Abbas, A.M., Elkaffas, S.M., El-Tobely, T.A., Khamis, S., Abd El-Samie, F.E.: Simultaneous fusion and denoising of panchromatic and multispectral satellite images. Sens. Imaging Int. J. 13(3), 119\u2013141 (2012)","journal-title":"Sens. Imaging Int. J."},{"issue":"5","key":"2628_CR8","doi-asserted-by":"publisher","first-page":"711","DOI":"10.15837\/ijccc.2019.5.3685","volume":"14","author":"XX Wang","year":"2019","unstructured":"Wang, X.X., Xu, Z.S., Dzitac, I.: Bibliometric analysis on research trends of international journal of computers communications & control. Int. J. Comput. Commun. Control 14(5), 711 (2019)","journal-title":"Int. J. Comput. Commun. Control"},{"issue":"6","key":"2628_CR9","first-page":"2319","volume":"3","author":"V Roopa","year":"2014","unstructured":"Roopa, V.: Remote sensing & its applications in disaster management like earthquake and tsunamis. Int. J. Sci. Res. (IJSR) 3(6), 2319\u20137064 (2014)","journal-title":"Int. J. Sci. Res. (IJSR)"},{"key":"2628_CR10","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.1007\/3-540-27468-5_74","volume-title":"Geo-Information for Disaster Management","author":"J Li","year":"2005","unstructured":"Li, J., Li, Y., Chapman, M.A.: High-resolution satellite image sources for disaster management in urban areas. In: Oosterom, P., Zlatanova, S., Fendel, E.M. (eds) Geo-Information for Disaster Management, pp. 1055\u20131070. Springer, Berlin, Heidelberg (2005)"},{"key":"2628_CR11","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/480274","author":"S Chen","year":"2013","unstructured":"Chen, S., Shi, W., Zhang, W.: an efficient universal noise removal algorithm combining spatial gradient and impulse statistic. Math. Probl. Eng. (2013). https:\/\/doi.org\/10.1155\/2013\/480274","journal-title":"Math. Probl. Eng."},{"issue":"2","key":"2628_CR12","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1007\/s10851-013-0476-x","volume":"49","author":"XY Wang","year":"2014","unstructured":"Wang, X.Y., Liu, Y.C., Yang, H.Y.: An efficient remote sensing image denoising method in extended discrete shearlet domain. J. Math. Imaging Vis. 49(2), 434\u2013453 (2014)","journal-title":"J. Math. Imaging Vis."},{"issue":"7","key":"2628_CR13","doi-asserted-by":"publisher","first-page":"1865","DOI":"10.1109\/TIP.2007.899598","volume":"16","author":"P Scheunders","year":"2007","unstructured":"Scheunders, P., De Backer, S.: Wavelet denoising of multicomponent images using Gaussian scale mixture models and a noise-free image as priors. IEEE Trans. Image Process. 16(7), 1865\u20131872 (2007)","journal-title":"IEEE Trans. Image Process."},{"issue":"8","key":"2628_CR14","doi-asserted-by":"publisher","first-page":"3855","DOI":"10.1109\/TSP.2008.921757","volume":"56","author":"C Chaux","year":"2008","unstructured":"Chaux, C., Duval, L., Benazza-Benyahia, A., Pesquet, J.C.: A nonlinear Stein-based estimator for multichannel image denoising. IEEE Trans. Signal Process. 56(8), 3855\u20133870 (2008)","journal-title":"IEEE Trans. Signal Process."},{"issue":"6","key":"2628_CR15","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1109\/LGRS.2013.2238603","volume":"10","author":"B Xue","year":"2013","unstructured":"Xue, B., Huang, Y., Yang, J., Shi, L., Zhan, Y., Cao, X.: Fast nonlocal remote sensing image denoising using cosine integral images. IEEE Geosci. Remote Sens. Lett. 10(6), 1309\u20131313 (2013)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"3","key":"2628_CR16","first-page":"442","volume":"13","author":"HK Aggarwal","year":"2016","unstructured":"Aggarwal, H.K., Majumdar, A.: Hyperspectral image denoising using spatio-spectral total variation. IEEE Geosci. Remote Sens. Lett. 13(3), 442\u2013446 (2016)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"2628_CR17","doi-asserted-by":"crossref","unstructured":"Xie, Q., Zhao, Q., Meng, D., Xu, Z., Gu, S., Zou, W., Zhang, L.: Multispectral images denoising by intrinsic tensor sparsity regularization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1692\u20131700. (2016)","DOI":"10.1109\/CVPR.2016.187"},{"issue":"11","key":"2628_CR18","doi-asserted-by":"publisher","first-page":"3892","DOI":"10.1109\/TGRS.2009.2031103","volume":"47","author":"A Duijster","year":"2009","unstructured":"Duijster, A., Scheunders, P., De Backer, S.: Wavelet-based EM algorithm for multispectral-image restoration. IEEE Trans. Geosci. Remote Sens. 47(11), 3892\u20133898 (2009)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"7","key":"2628_CR19","doi-asserted-by":"publisher","first-page":"169","DOI":"10.3390\/info9070169","volume":"9","author":"C Chen","year":"2018","unstructured":"Chen, C., Xu, Z.: Aerial-image denoising based on convolutional neural network with multi-scale residual learning approach. Information 9(7), 169 (2018)","journal-title":"Information"},{"issue":"6","key":"2628_CR20","doi-asserted-by":"publisher","first-page":"499","DOI":"10.3390\/rs8060499","volume":"8","author":"H Lu","year":"2016","unstructured":"Lu, H., Wei, J., Wang, L., Liu, P., Liu, Q., Wang, Y., Deng, X.: Reference information based remote sensing image reconstruction with generalized nonconvex low-rank approximation. Remote Sens. 8(6), 499 (2016)","journal-title":"Remote Sens."},{"issue":"7","key":"2628_CR21","doi-asserted-by":"publisher","first-page":"1985","DOI":"10.3390\/s18071985","volume":"18","author":"R Wang","year":"2018","unstructured":"Wang, R., Xiao, X., Guo, B., Qin, Q., Chen, R.: An effective image denoising method for UAV images via improved generative adversarial networks. Sensors 18(7), 1985 (2018)","journal-title":"Sensors"},{"key":"2628_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.07.025","author":"C Tian","year":"2020","unstructured":"Tian, C., Fei, L., Zheng, W., Xu, Y., Zuo, W., Lin, C.W.: Deep learning on image denoising: an overview. Neural Netw. (2020). https:\/\/doi.org\/10.1016\/j.neunet.2020.07.025","journal-title":"Neural Netw."},{"issue":"1","key":"2628_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11760-019-01537-x","volume":"15","author":"R Lan","year":"2021","unstructured":"Lan, R., Zou, H., Pang, C., Zhong, Y., Liu, Z., Luo, X.: Image denoising via deep residual convolutional neural networks. Signal Image Video Process. 15(1), 1\u20138 (2021)","journal-title":"Signal Image Video Process."},{"issue":"1","key":"2628_CR24","doi-asserted-by":"publisher","first-page":"101","DOI":"10.3390\/rs13010101","volume":"13","author":"NA Golilarz","year":"2021","unstructured":"Golilarz, N.A., Gao, H., Pirasteh, S., Yazdi, M., Zhou, J., Fu, Y.: Satellite multispectral and hyperspectral image de-noising with enhanced adaptive generalized Gaussian distribution threshold in the wavelet domain. Remote Sens. 13(1), 101 (2021)","journal-title":"Remote Sens."},{"issue":"16","key":"2628_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12517-020-05798-6","volume":"13","author":"AK Bhandari","year":"2020","unstructured":"Bhandari, A.K., Kumar, D., Kumar, A.: Intrascale windowing-based cuckoo search\u2013optimized sub-band thresholding for satellite image denoising. Arab. J. Geosci. 13(16), 1\u201318 (2020)","journal-title":"Arab. J. Geosci."},{"issue":"1","key":"2628_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1687-5281-2014-41","volume":"2014","author":"M Kadiri","year":"2014","unstructured":"Kadiri, M., Djebbouri, M., Carr\u00e9, P.: Magnitude-phase of the dual-tree quaternionic wavelet transform for multispectral satellite image denoising. Eurasip J. Image Video Process. 2014(1), 1\u201316 (2014)","journal-title":"Eurasip J. Image Video Process."},{"key":"2628_CR27","doi-asserted-by":"crossref","unstructured":"Monika, R., Bala, A.A., Suvarnamma, A.: Block compressive sampling and wiener curvelet denoising approach for satellite images. In: International Conference on Intelligent Computing and Applications, pp. 11\u201318. Springer, Singapore (2018)","DOI":"10.1007\/978-981-10-5520-1_2"},{"key":"2628_CR28","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/3296874","author":"A Carrio","year":"2017","unstructured":"Carrio, A., Sampedro, C., Rodriguez-Ramos, A., Campoy, P.: A review of deep learning methods and applications for unmanned aerial vehicles. J. Sens. (2017). https:\/\/doi.org\/10.1155\/2017\/3296874","journal-title":"J. Sens."},{"issue":"18","key":"2628_CR29","doi-asserted-by":"publisher","first-page":"3929","DOI":"10.3390\/s19183929","volume":"19","author":"G Tsagkatakis","year":"2019","unstructured":"Tsagkatakis, G., Aidini, A., Fotiadou, K., Giannopoulos, M., Pentari, A., Tsakalides, P.: Survey of deep-learning approaches for remote sensing observation enhancement. Sensors 19(18), 3929 (2019)","journal-title":"Sensors"},{"key":"2628_CR30","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":"3","key":"2628_CR31","doi-asserted-by":"publisher","first-page":"273","DOI":"10.15837\/ijccc.2009.3.2435","volume":"4","author":"L Pan","year":"2009","unstructured":"Pan, L., P\u0103un, G.: Spiking neural P systems with anti-spikes. Int. J. Comput. Commun. Control 4(3), 273\u2013282 (2009)","journal-title":"Int. J. Comput. Commun. Control"},{"issue":"7","key":"2628_CR32","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."},{"key":"2628_CR33","doi-asserted-by":"crossref","unstructured":"Mehmood, A.: Image denoising using convolutional neural network. In: Pattern Recognition and Tracking XXXI. International Society for Optics and Photonics, vol. 11400, p. 114000A. (2020)","DOI":"10.1117\/12.2563838"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02628-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-023-02628-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02628-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T18:12:59Z","timestamp":1694542379000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-023-02628-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,24]]},"references-count":33,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["2628"],"URL":"https:\/\/doi.org\/10.1007\/s11760-023-02628-6","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,24]]},"assertion":[{"value":"13 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This paper has no conflict of interest for publishing.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"My research guide reviewed and ethically approved this manuscript for publishing in this journal.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"This article does not contain any studies with human or animal subjects performed by any of the authors.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal rights"}},{"value":"I certify that I have explained the nature and purpose of this study to the above-named individual, and I have discussed the potential benefits of this study participation. The questions the individual had about this study have been answered, and we will always be available to address future questions.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}