0468355 20240103213304.120170105235959.9 84991201811 000389784400014 10.1016/j.dsp.2016.09.007 12 s. P cav_un_epca*02527191051-2004601 (2017)140-151Elsevier signal separation painted layer enhancement Artwork analysis cav_un_auth*0254045 Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Blažek Jan UTIA-B CZ K Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0340366 Striová J. IT cav_un_auth*0340367 Fontana R. IT cav_un_auth*0101238 Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Zitová Barbara UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2016/ZOI/zitova-0468355.pdf cav_un_auth*0285028 GAP103/12/2211 GA ČR cav_un_auth*0340368 FP7-ICT-2011.8.2 ICT EC IT Near Infrared (NIR) reflectography, coupled to visible (VIS) one, is a spectrophotometric imaging technique employed to probe both the inner and the outer layers of artworks. NIR reflectograms may partially contain information pertinent to the visible spectrum (due to the poor pigment transparency in NIR) and this decreases their comprehensibility. This work presents an innovative digital processing methodology for accentuating information contained in the infrared reflectograms. The proposed method consists of inducing minor changes in pixel intensity by suppressing VIS information content from NIR information content. The method creates such enhanced NIR reflectogram by extrapolating VIS reflectogram to a reflectogram recorded in NIR range and by subtracting it from the measured values in the near infrared spectral sub-band. As an extrapolator we suggest a feed forward artificial neural network (ANN). Significant results of improved visualization are exemplified on reflectograms acquired with a VIS-NIR 〈400,2250〉 nm〈400,2250〉 nm scanning device on real paintings such as Madonna dei Fusi attributed to Leonardo da Vinci. Parameters of the method, artificial neural network and separability of used pigments are discussed. JC 10000 10200 10201 2017 4 4 A hod 4ah 20231122142146.0 RVO:67985556 http://hdl.handle.net/11104/0267806 1 cav_un_auth*0340369 National Institute of Optics INO IT 1 Department of Image Processing UTIA-B 10200 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS S RIV/67985556:_____/17:00468355!RIV17-AV0-67985556 191870809 doplneni OECD UTIA-B RIV/67985556:_____/17:00468355!RIV17-GA0-67985556 191876839 doplneni OECD UTIA-B 3+4 Article Engineering Electrical Electronic 3+4 Article Engineering Electrical Electronic 3+4 Article Engineering Electrical Electronic ENGINEERING.ELECTRICAL&ELECTRONIC 2.216 0.689 6 0.00591 0.52 3915 0.550 1.953 196 Q2 42.989 60.2 Q3 Q2 0.82 Q2 60.192 2017 Soubory v repozitáři: zitova-0468355.pdf 84991201811 SCOPUS PUBMED 000389784400014 WOS cav_un_epca*0252719 Digital Signal Processing 1051-2004 1095-4333 Roč. 60 č. 1 2017 140 151 Elsevier