{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T06:24:16Z","timestamp":1749536656766},"publisher-location":"Cham","reference-count":37,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030305079"},{"type":"electronic","value":"9783030305086"}],"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\/978-3-030-30508-6_1","type":"book-chapter","created":{"date-parts":[[2019,9,8]],"date-time":"2019-09-08T19:02:47Z","timestamp":1567969367000},"page":"3-16","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Unsharp Masking Layer: Injecting Prior Knowledge in Convolutional Networks for Image Classification"],"prefix":"10.1007","author":[{"given":"Jose","family":"Carranza-Rojas","sequence":"first","affiliation":[]},{"given":"Saul","family":"Calderon-Ramirez","sequence":"additional","affiliation":[]},{"given":"Ad\u00e1n","family":"Mora-Fallas","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Granados-Menani","sequence":"additional","affiliation":[]},{"given":"Jordina","family":"Torrents-Barrena","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"issue":"2","key":"1_CR1","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1007\/s13319-018-0164-0","volume":"9","author":"Z Al-Ameen","year":"2018","unstructured":"Al-Ameen, Z.: Sharpness improvement for medical images using a new nimble filter. 3D Res. 9(2), 12 (2018)","journal-title":"3D Res."},{"key":"1_CR2","unstructured":"Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 60\u201365. IEEE (2005)"},{"issue":"1","key":"1_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00211-006-0029-y","volume":"105","author":"A Buades","year":"2006","unstructured":"Buades, A., Coll, B., Morel, J.M.: Neighborhood filters and pdes. Numer. Math. 105(1), 1\u201334 (2006)","journal-title":"Numer. Math."},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Calderon, S., et al.: Assessing the impact of the deceived non local means filter as a preprocessing stage in a convolutional neural network based approach for age estimation using digital hand x-ray images. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 1752\u20131756. IEEE (2018)","DOI":"10.1109\/ICIP.2018.8451191"},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Calder\u00f3n, S., Moya, D., Cruz, J.C., Valverde, J.M.: A first glance on the enhancement of digital cell activity videos from glioblastoma cells with nuclear staining. In: 2016 IEEE 36th Central American and Panama Convention (CONCAPAN XXXVI), pp. 1\u20136. IEEE (2016)","DOI":"10.1109\/CONCAPAN.2016.7942344"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Chan, T.F., Shen, J.J.: Image processing and analysis: variational, PDE, wavelet, and stochastic methods, vol. 94. SIAM (2005)","DOI":"10.1137\/1.9780898717877"},{"key":"1_CR7","unstructured":"da Costa, G.B.P., Contato, W.A., Nazare, T.S., Neto, J., Ponti, M.: An empirical study on the effects of different types of noise in image classification tasks. In: Iberoamerican Conference on Pattern Recognition 2017, abs\/1609.02781, pp. 416\u2013424 (2016)"},{"issue":"8","key":"1_CR8","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."},{"key":"1_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-642-15816-2_1","volume-title":"Biomedical Image Processing","author":"TM Deserno","year":"2010","unstructured":"Deserno, T.M.: Fundamentals of biomedical image processing. In: Deserno, T. (ed.) Biomedical Image Processing, pp. 1\u201351. Springer, Heidelberg (2010). \n                      https:\/\/doi.org\/10.1007\/978-3-642-15816-2_1"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Dodge, S., Karam, L.: Understanding how image quality affects deep neural networks. In: 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX), pp. 1\u20136. IEEE (2016)","DOI":"10.1109\/QoMEX.2016.7498955"},{"key":"1_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1007\/978-3-319-46448-0_36","volume-title":"Computer Vision \u2013 ECCV 2016","author":"R Gadde","year":"2016","unstructured":"Gadde, R., Jampani, V., Kiefel, M., Kappler, D., Gehler, P.V.: Superpixel convolutional networks using bilateral inceptions. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 597\u2013613. Springer, Cham (2016). \n                      https:\/\/doi.org\/10.1007\/978-3-319-46448-0_36"},{"key":"1_CR12","unstructured":"Go\u00ebau, H., Bonnet, P., Joly, A.: LifeCLEF plant identification task 2015. In: CLEF: Conference and Labs of the Evaluation forum. CLEF 2015 Working notes, vol. 1391, Toulouse, France. CEUR-WS, September 2015"},{"key":"1_CR13","unstructured":"Goeau, H., Bonnet, P., Joly, A.: Plant identification based on noisy web data: the amazing performance of deep learning (LifeCLEF 2017). In: CLEF 2017 - Conference and Labs of the Evaluation Forum, Dublin, Ireland, pp. 1\u201313, September 2017"},{"key":"1_CR14","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1_CR16","unstructured":"Iandola, F.N., Moskewicz, M.W., Ashraf, K., Han, S., Dally, W.J., Keutzer, K.: Squeezenet: alexnet-level accuracy with 50x fewer parameters and \n                      \n                        \n                      \n                      $$<$$\n                    1 MB model size. CoRR, abs\/1602.07360 (2016)"},{"key":"1_CR17","volume-title":"Fundamentals of Digital Image Processing","author":"AK Jain","year":"1989","unstructured":"Jain, A.K.: Fundamentals of Digital Image Processing. Prentice Hall, Englewood Cliffs (1989)"},{"key":"1_CR18","doi-asserted-by":"crossref","unstructured":"Jampani, V., Kiefel, M., Gehler, P.V.: Learning sparse high dimensional filters: image filtering, dense CRFs and bilateral neural networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4452\u20134461 (2016)","DOI":"10.1109\/CVPR.2016.482"},{"key":"1_CR19","unstructured":"Krizhevsky, A., Nair, V., Hinton, G.: CIFAR-100 (Canadian Institute for Advanced Research)"},{"issue":"11","key":"1_CR20","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y. Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. In: Proceedings of the IEEE, pp. 2278\u20132324 (1998)","journal-title":"Proceedings of the IEEE"},{"key":"1_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10278-016-9926-5","volume":"30","author":"H Lee","year":"2017","unstructured":"Lee, H., et al.: Fully automated deep learning system for bone age assessment. J. Digit. Imaging 30, 1\u201315 (2017)","journal-title":"J. Digit. Imaging"},{"key":"1_CR22","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.cmpb.2017.09.010","volume":"152","author":"MS Lee","year":"2017","unstructured":"Lee, M.S., Park, C.H., Kang, M.G.: Edge enhancement algorithm for low-dose X-ray fluoroscopic imaging. Comput. Methods Programs Biomed. 152, 45\u201352 (2017)","journal-title":"Comput. Methods Programs Biomed."},{"key":"1_CR23","doi-asserted-by":"crossref","unstructured":"Lu, H., Wang, H., Zhang, Q., Won, D., Yoon, S.W.: A dual-tree complex wavelet transform based convolutional neural network for human thyroid medical image segmentation. In: 2018 IEEE International Conference on Healthcare Informatics (ICHI), pp. 191\u2013198. IEEE (2018)","DOI":"10.1109\/ICHI.2018.00029"},{"key":"1_CR24","series-title":"IFIP Advances in Information and Communication Technology","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1007\/978-3-319-44447-5_3","volume-title":"ICT for Promoting Human Development and Protecting the Environment","author":"E Mata-Montero","year":"2016","unstructured":"Mata-Montero, E., Carranza-Rojas, J.: Automated plant species identification: challenges and opportunities. In: Mata, F.J., Pont, A. (eds.) WITFOR 2016. IAICT, vol. 481, pp. 26\u201336. Springer, Cham (2016). \n                      https:\/\/doi.org\/10.1007\/978-3-319-44447-5_3"},{"key":"1_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1007\/978-3-319-75193-1_50","volume-title":"Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications","author":"TS Nazar\u00e9","year":"2018","unstructured":"Nazar\u00e9, T.S., da Costa, G.B.P., Contato, W.A., Ponti, M.: Deep convolutional neural networks and noisy images. In: Mendoza, M., Velast\u00edn, S. (eds.) CIARP 2017. LNCS, vol. 10657, pp. 416\u2013424. Springer, Cham (2018). \n                      https:\/\/doi.org\/10.1007\/978-3-319-75193-1_50"},{"key":"1_CR26","unstructured":"Paszke, A., et al.: Automatic differentiation in pytorch (2017)"},{"issue":"3","key":"1_CR27","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1109\/83.826787","volume":"9","author":"A Polesel","year":"2000","unstructured":"Polesel, A., Ramponi, G., Mathews, V.J.: Image enhancement via adaptive unsharp masking. IEEE Trans. Image Process. 9(3), 505\u2013510 (2000)","journal-title":"IEEE Trans. Image Process."},{"issue":"1\u20134","key":"1_CR28","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","volume":"60","author":"LI Rudin","year":"1992","unstructured":"Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60(1\u20134), 259\u2013268 (1992)","journal-title":"Physica D"},{"issue":"3","key":"1_CR29","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"key":"1_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-011-1040-2","volume-title":"Mathematical Morphology and Its Applications to Image Processing","author":"J Serra","year":"2012","unstructured":"Serra, J., Soille, P.: Mathematical Morphology and Its Applications to Image Processing, vol. 2. Springer, Dordrecht (2012). \n                      https:\/\/doi.org\/10.1007\/978-94-011-1040-2"},{"issue":"1","key":"1_CR31","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/s11760-013-0505-7","volume":"8","author":"TS Sharmila","year":"2014","unstructured":"Sharmila, T.S., Ramar, K., Raja, T.S.R.: Impact of applying pre-processing techniques for improving classification accuracy. SIViP 8(1), 149\u2013157 (2014)","journal-title":"SIViP"},{"issue":"5","key":"1_CR32","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1007\/s11760-016-1022-2","volume":"11","author":"NK Singh","year":"2017","unstructured":"Singh, N.K., Sunaniya, A.K.: An adaptive image sharpening scheme based on local intensity variations. SIViP 11(5), 777\u2013784 (2017)","journal-title":"SIViP"},{"key":"1_CR33","unstructured":"Strobel, N., Mitra, S.K.: Quadratic filters for image contrast enhancement. In: 1994 Conference Record of the Twenty-Eighth Asilomar Conference on Signals, Systems and Computers, vol. 1, pp. 208\u2013212. IEEE (1994)"},{"key":"1_CR34","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818\u20132826, June 2016","DOI":"10.1109\/CVPR.2016.308"},{"key":"1_CR35","unstructured":"Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Sixth International Conference on Computer Vision, pp. 839\u2013846. IEEE (1998)"},{"key":"1_CR36","unstructured":"Weickert, J.: Anisotropic Diffusion in Image Processing, vol. 1. Teubner Stuttgart (1998)"},{"issue":"9","key":"1_CR37","doi-asserted-by":"publisher","first-page":"4465","DOI":"10.1109\/TIP.2018.2838660","volume":"27","author":"W Ye","year":"2018","unstructured":"Ye, W., Ma, K.-K.: Blurriness-guided unsharp masking. IEEE Trans. Image Process. 27(9), 4465\u20134477 (2018)","journal-title":"IEEE Trans. Image Process."}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Image Processing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30508-6_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,8]],"date-time":"2019-09-08T19:17:54Z","timestamp":1567970274000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-30508-6_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030305079","9783030305086"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30508-6_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"9 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}