{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:05:11Z","timestamp":1740107111724,"version":"3.37.3"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2022,2,26]],"date-time":"2022-02-26T00:00:00Z","timestamp":1645833600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,2,26]],"date-time":"2022-02-26T00:00:00Z","timestamp":1645833600000},"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":["Vis Comput"],"DOI":"10.1007\/s00371-022-02405-5","type":"journal-article","created":{"date-parts":[[2022,2,26]],"date-time":"2022-02-26T18:02:21Z","timestamp":1645898541000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Regression layer-based convolution neural network for synthetic aperture radar images: de-noising and super-resolution"],"prefix":"10.1007","author":[{"given":"Aiman","family":"Mousa","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasser","family":"Badran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gouda","family":"Salama","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5714-4596","authenticated-orcid":false,"given":"Tarek","family":"Mahmoud","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,2,26]]},"reference":[{"issue":"12","key":"2405_CR1","doi-asserted-by":"publisher","first-page":"6919","DOI":"10.1109\/TGRS.2016.2588325","volume":"54","author":"S-W Chen","year":"2016","unstructured":"Chen, S.-W., Wang, X.-S., Sato, M.: Urban damage level mapping based on scattering mechanism investigation using fully polarimetric SAR data for the 3.11 East Japan Earthquake. IEEE Trans. Geosci. Remote Sens. 54(12), 6919\u20136929 (2016). https:\/\/doi.org\/10.1109\/TGRS.2016.2588325","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"8","key":"2405_CR2","doi-asserted-by":"publisher","first-page":"2657","DOI":"10.1109\/JSTARS.2018.2818939","volume":"11","author":"S-W Chen","year":"2018","unstructured":"Chen, S.-W., Wang, X.-S., Xiao, S.-P.: Urban damage level mapping based on co-polarization coherence pattern using multitemporal polarimetric SAR data. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 11(8), 2657\u20132667 (2018). https:\/\/doi.org\/10.1109\/JSTARS.2018.2818939","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"issue":"4","key":"2405_CR3","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1109\/LGRS.2018.2799877","volume":"15","author":"S-W Chen","year":"2018","unstructured":"Chen, S.-W., Tao, C.-S.: PolSAR image classification using polarimetric-feature-driven deep convolutional neural network. IEEE Geosci. Remote Sens. Lett. 15(4), 627\u2013631 (2018). https:\/\/doi.org\/10.1109\/LGRS.2018.2799877","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"2405_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2514\/6.2016-5353","volume":"5353","author":"BA Massinas","year":"2016","unstructured":"Massinas, B.A., Doulamis, A., Doulamis, N., Paradissis, D.: Ensemble classifiers in optimal estimation for ionospheric disturbances behavior on spaceborne interferometric SAR systems. Proceedings SPACE Conf., AIAA, Long Beach, California, USA 5353, 1\u201311 (2016). https:\/\/doi.org\/10.2514\/6.2016-5353","journal-title":"Proceedings SPACE Conf., AIAA, Long Beach, California, USA"},{"issue":"9","key":"2405_CR5","doi-asserted-by":"publisher","first-page":"7194","DOI":"10.1109\/TGRS.2019.2912153","volume":"57","author":"PA Penna","year":"2019","unstructured":"Penna, P.A., Mascarenhas, N.D.: SAR speckle nonlocal filtering with statistical modeling of haar wavelet coefficients and stochastic distances. IEEE Trans. Geosci. Remote Sens. 57(9), 7194\u20137208 (2019). https:\/\/doi.org\/10.1109\/TGRS.2019.2912153","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"2405_CR6","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1109\/JSTARS.2020.2973920","volume":"13","author":"M Yahia","year":"2020","unstructured":"Yahia, M., Ali, T., Mortula, M.M., Abdelfattah, R., El Mahdy, S., Arampola, N.S.: Enhancement of SAR speckle denoising using the improved iterative filter. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 13, 859\u2013871 (2020). https:\/\/doi.org\/10.1109\/JSTARS.2020.2973920","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"key":"2405_CR7","doi-asserted-by":"publisher","unstructured":"Yang, Y., Ding, Z., Liu, J., Gao, Q., Yuan, X., and Lu, X.: An adaptive SAR image speckle reduction algorithm based on wavelet transform and diffusion equations for marine scenes. Proceedings of the International Geoscience and Remote Sensing Symposium, IEEE, Fort Worth, TX, USA, 2017, pp. 3082\u20133085. https:\/\/doi.org\/10.1109\/IGARSS.2017.8127650","DOI":"10.1109\/IGARSS.2017.8127650"},{"issue":"4","key":"2405_CR8","doi-asserted-by":"publisher","first-page":"2388","DOI":"10.1109\/TGRS.2012.2211366","volume":"51","author":"H-C Li","year":"2013","unstructured":"Li, H.-C., Hong, W., Wu, Y.-R., Fan, P.-Z.: Bayesian wavelet shrinkage with heterogeneity-adaptive threshold for SAR image despeckling based on generalized gamma distribution. IEEE Trans. Geosci. Remote Sens. 51(4), 2388\u20132402 (2013). https:\/\/doi.org\/10.1109\/TGRS.2012.2211366","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"2405_CR9","doi-asserted-by":"publisher","unstructured":"Abergel, R., Denis, L., Tupin, F., Ladjal, S., Deledalle, C-A., and Almansa, A.: Resolution-preserving speckle reduction of SAR images: the benefits of speckle decorrelation and targets extraction. Proceedings of the International Symposium on Geoscience and Remote Sensing (IGARSS), IEEE, Japan, 2019, pp. 608\u2013611. https:\/\/doi.org\/10.1109\/IGARSS.2019.8900036","DOI":"10.1109\/IGARSS.2019.8900036"},{"issue":"7","key":"2405_CR10","doi-asserted-by":"publisher","first-page":"1965","DOI":"10.1007\/s00371-020-01957-8","volume":"37","author":"G Rohith","year":"2021","unstructured":"Rohith, G., Kumar, L.S.: Paradigm shifts in super-resolution techniques for remote sensing applications. Vis. Comput. 37(7), 1965\u20132008 (2021). https:\/\/doi.org\/10.1007\/s00371-020-01957-8","journal-title":"Vis. Comput."},{"issue":"8","key":"2405_CR11","doi-asserted-by":"publisher","first-page":"1573","DOI":"10.1007\/s00371-019-01756-w","volume":"36","author":"E Mikaeli","year":"2020","unstructured":"Mikaeli, E., Aghagolzadeh, A., Azghani, M.: Single-image super-resolution via patch-based and group-based local smoothness modeling. Vis. Comput. 36(8), 1573\u20131589 (2020). https:\/\/doi.org\/10.1007\/s00371-019-01756-w","journal-title":"Vis. Comput."},{"key":"2405_CR12","doi-asserted-by":"publisher","unstructured":"Timofte, R., De Smet, V., and Van Gool, L.: A+: Adjusted anchored neighborhood regression for fast super-resolution. Proceedings of the 12th Asian Conference on Computer Vision (ACCV), Springer, Singapore, 2014, pp. 111\u2013126. https:\/\/doi.org\/10.1007\/978-3-319-16817-3_8","DOI":"10.1007\/978-3-319-16817-3_8"},{"key":"2405_CR13","doi-asserted-by":"publisher","unstructured":"Yang, C.-Y., and Yang, M.-H.: fast direct super-resolution by simple functions. Proceedings of the International Conference on Computer Vision, IEEE, Sydney, NSW, Australia, 2013, pp. 561\u2013568. https:\/\/doi.org\/10.1109\/ICCV.2013.75","DOI":"10.1109\/ICCV.2013.75"},{"key":"2405_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-020-02007-z","author":"D Zhou","year":"2020","unstructured":"Zhou, D., Liu, Y., Li, X., Zhang, C.: Single-image super-resolution based on local biquadratic spline with edge constraints and adaptive optimization in transform domain. Vis. Comput. (2020). https:\/\/doi.org\/10.1007\/s00371-020-02007-z","journal-title":"Vis. Comput."},{"issue":"9","key":"2405_CR15","doi-asserted-by":"publisher","first-page":"2797","DOI":"10.1109\/TIP.2015.2431435","volume":"24","author":"Y Zhang","year":"2015","unstructured":"Zhang, Y., Liu, J., Yang, W., Guo, Z.: Image super-resolution based on structure-modulated sparse representation. IEEE Trans. Image Process. 24(9), 2797\u20132810 (2015). https:\/\/doi.org\/10.1109\/TIP.2015.2431435","journal-title":"IEEE Trans. Image Process."},{"key":"2405_CR16","doi-asserted-by":"publisher","first-page":"121350","DOI":"10.1109\/ACCESS.2019.2936455","volume":"7","author":"A Ahmed","year":"2019","unstructured":"Ahmed, A., Kun, S., Memon, R.A., Ahmed, J., Tefera, G.: Convolutional sparse coding using wavelets for single image super-resolution. IEEE Access 7, 121350\u2013121359 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2936455","journal-title":"IEEE Access"},{"key":"2405_CR17","doi-asserted-by":"publisher","unstructured":"Dong, C., Loy, C. C., He, K., and Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision \u2013 ECCV Lecture Notes in Computer Science, Vol. 8692, Springer, Cham., 2014, pp. 184\u2013199. https:\/\/doi.org\/10.1007\/978-3-319-10593-2_13","DOI":"10.1007\/978-3-319-10593-2_13"},{"issue":"7","key":"2405_CR18","doi-asserted-by":"publisher","first-page":"4590","DOI":"10.1109\/TGRS.2020.2964288","volume":"58","author":"S Mei","year":"2020","unstructured":"Mei, S., Jiang, R., Li, X., Du, Q.: Spatial and spectral joint super-resolution using convolutional neural network. IEEE Trans. Geosci. Remote Sens. 58(7), 4590\u20134603 (2020). https:\/\/doi.org\/10.1109\/TGRS.2020.2964288","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"2405_CR19","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., and Fu, Y.: Residual dense network for image super-resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Salt Lake City, UT, USA, 2018, pp. 2472\u20132481. https:\/\/doi.org\/10.1109\/CVPR.2018.00262","DOI":"10.1109\/CVPR.2018.00262"},{"key":"2405_CR20","doi-asserted-by":"publisher","unstructured":"Tai, Y., Yang, J., and Liu, X.: Image super-resolution via deep recursive residual network. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Honolulu, HI, USA, 2017, pp. 2790\u20132798. https:\/\/doi.org\/10.1109\/CVPR.2017.298","DOI":"10.1109\/CVPR.2017.298"},{"key":"2405_CR21","doi-asserted-by":"publisher","unstructured":"Johnson, J., Alahi, A., and Fei-Fei, L.: perceptual losses for real-time style transfer and super-resolution. In: Leibe B., Matas J., Sebe N., and Welling M. (eds) Computer Vision \u2013 Proceedings of the European Conference on Computer Vision (ECCV), 2016, Lecture Notes in Computer Science, Vol. 9906, Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-46475-6_43","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"2405_CR22","unstructured":"Dosovitskiy, A., and Brox, T.: Generating images with perceptual similarity metrics based on deep networks. Proceedings of the 30th International Conference on Neural Information Processing Systems (NIPS), ACM, Barcelona, Spain, 2016, pp. 658\u2013666"},{"key":"2405_CR23","doi-asserted-by":"publisher","unstructured":"Yuan, Y., Liu, S., Zhang, J., Zhang, Y., Dong, C., and Lin, L.: Unsupervised image super-resolution using cycle-in-cycle generative adversarial networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE\/CVF, Salt Lake City, UT, USA, 2018, pp. 81400\u201381409. https:\/\/doi.org\/10.1109\/CVPRW.2018.00113","DOI":"10.1109\/CVPRW.2018.00113"},{"key":"2405_CR24","doi-asserted-by":"publisher","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., and Shi, W.: Photo-realistic single image super-resolution using a generative adversarial network. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Honolulu, HI, USA, 2017, pp. 105\u2013114. https:\/\/doi.org\/10.1109\/CVPR.2017.19","DOI":"10.1109\/CVPR.2017.19"},{"issue":"5","key":"2405_CR25","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1007\/s00371-020-01843-3","volume":"37","author":"T Ma","year":"2021","unstructured":"Ma, T., Tian, W.: Back-projection-based progressive growing generative adversarial network for single image super-resolution. Vis. Comput. 37(5), 925\u2013938 (2021). https:\/\/doi.org\/10.1007\/s00371-020-01843-3","journal-title":"Vis. Comput."},{"issue":"8","key":"2405_CR26","doi-asserted-by":"publisher","first-page":"2285","DOI":"10.1007\/s00371-020-01986-3","volume":"37","author":"H Song","year":"2021","unstructured":"Song, H., Wang, M., Zhang, L., Li, Y., Jiang, Z., Yin, G.: S2RGAN: Sonar-image super-resolution based on generative adversarial network. Vis. Comput. 37(8), 2285\u20132299 (2021). https:\/\/doi.org\/10.1007\/s00371-020-01986-3","journal-title":"Vis. Comput."},{"issue":"4","key":"2405_CR27","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). https:\/\/doi.org\/10.1109\/TIP.2003.819861","journal-title":"IEEE Trans. Image Process."},{"key":"2405_CR28","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1016\/j.sigpro.2016.05.002","volume":"128","author":"L Yue","year":"2016","unstructured":"Yue, L., Shen, H., Li, J., Yuan, Q., Zhang, H., Zhang, L.: Image super-resolution: The techniques, applications, and future. Signal Process. 128, 389\u2013408 (2016). https:\/\/doi.org\/10.1016\/j.sigpro.2016.05.002","journal-title":"Signal Process."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-022-02405-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-022-02405-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-022-02405-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,26]],"date-time":"2022-02-26T18:02:51Z","timestamp":1645898571000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-022-02405-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,26]]},"references-count":28,"alternative-id":["2405"],"URL":"https:\/\/doi.org\/10.1007\/s00371-022-02405-5","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"type":"print","value":"0178-2789"},{"type":"electronic","value":"1432-2315"}],"subject":[],"published":{"date-parts":[[2022,2,26]]},"assertion":[{"value":"7 January 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 February 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}