{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:12:12Z","timestamp":1740100332981,"version":"3.37.3"},"reference-count":44,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T00:00:00Z","timestamp":1631059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T00:00:00Z","timestamp":1631059200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T00:00:00Z","timestamp":1631059200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100009950","name":"Ministry of Education","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009950","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,8]]},"DOI":"10.1109\/ichms53169.2021.9582444","type":"proceedings-article","created":{"date-parts":[[2021,10,27]],"date-time":"2021-10-27T20:07:19Z","timestamp":1635365239000},"page":"1-6","source":"Crossref","is-referenced-by-count":3,"title":["Perception-Aware Losses Facilitate CT Denoising and Artifact Removal"],"prefix":"10.1109","author":[{"given":"Suhita","family":"Ghosh","sequence":"first","affiliation":[]},{"given":"Andreas","family":"Krug","sequence":"additional","affiliation":[]},{"given":"Georg","family":"Rose","sequence":"additional","affiliation":[]},{"given":"Sebastian","family":"Stober","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"article-title":"Digital image processing","year":"2002","author":"gonzalez","key":"ref39"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-72943-3_10"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2011.6116175"},{"key":"ref32","article-title":"On between-coefficient contrast masking of dct basis functions","volume":"4","author":"ponomarenko","year":"2007","journal-title":"Proceedings of the Third International Workshop on Video Processing and Quality Metrics"},{"key":"ref31","first-page":"3933","article-title":"Faster neural networks straight from jpeg","volume":"31","author":"gueguen","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref30","article-title":"On using cnn with dct based image data","volume":"2","author":"ulicny","year":"2017","journal-title":"Proceedings of the 19th Irish Machine Vision and Image Processing conference IMVIP"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.93"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2008.930649"},{"article-title":"Understanding posterior collapse in generative latent variable models","year":"2019","author":"lucas","key":"ref35"},{"key":"ref34","article-title":"Taming vaes","author":"rezende","year":"2018","journal-title":"arXiv preprint arXiv 1810 10053"},{"key":"ref10","first-page":"10","article-title":"Deep admm-net for compressive sensing mri","author":"yang","year":"2016","journal-title":"Proceedings of the 30th International Conference on Neural Information Processing Systems"},{"key":"ref40","article-title":"Low dose ct image and projection data [data set]","author":"mccollough","year":"2020","journal-title":"The Cancer Imaging Archive"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2832007"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2832656"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1038\/nature25988"},{"key":"ref14","article-title":"Deep learning based computed tomography whys and wherefores","author":"bazrafkan","year":"2019","journal-title":"arXiv preprint arXiv 1904 12848"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-019-0466-4"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-61598-7_12"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2823338"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-25153-w"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1117\/12.2512597"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2857824"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-018-5810-7"},{"key":"ref27","article-title":"Fighting deepfakes by detecting gan dct anomalies","author":"giudice","year":"2021","journal-title":"arXiv preprint arXiv 2101 06286"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s00247-011-2102-7"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1364\/JOSAA.1.000612"},{"key":"ref29","article-title":"Harmonic convolutional networks based on discrete cosine transform","author":"ulicny","year":"2020","journal-title":"arXiv preprint arXiv 2001 04786"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-017-4904-y"},{"key":"ref8","article-title":"Neural discrete representation learning","author":"oord","year":"2017","journal-title":"arXiv preprint arXiv 1711 00540"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s40305-019-00287-4"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-7657-3_12"},{"key":"ref9","article-title":"Generating diverse high-fidelity images with vq-vae-2","author":"razavi","year":"2019","journal-title":"arXiv preprint arXiv 1906 03008"},{"journal-title":"Health at a Glance Europe 2020 State of Health in the EU Cycle","year":"2020","key":"ref1"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1364\/BOE.8.000679"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13258"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2715284"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1117\/12.2549773"},{"key":"ref24","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"arXiv preprint arXiv 1409 1556"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1117\/12.2534517"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2827462"},{"key":"ref44","first-page":"3","article-title":"Rectifier nonlinearities improve neural network acoustic models","volume":"30","author":"maas","year":"2013","journal-title":"Proc ICML"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2005.844909"},{"key":"ref43","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"arXiv preprint arXiv 1412 6980"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00181"}],"event":{"name":"2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS)","start":{"date-parts":[[2021,9,8]]},"location":"Magdeburg, Germany","end":{"date-parts":[[2021,9,10]]}},"container-title":["2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9582416\/9582443\/09582444.pdf?arnumber=9582444","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T16:56:01Z","timestamp":1652201761000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9582444\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,8]]},"references-count":44,"URL":"https:\/\/doi.org\/10.1109\/ichms53169.2021.9582444","relation":{},"subject":[],"published":{"date-parts":[[2021,9,8]]}}}