Digital restoration of colour cinematic films using imaging spectroscopy and machine learning. Liu, L., Catelli, E., Katsaggelos, A., Sciutto, G., Mazzeo, R., Milanic, M., Stergar, J., Prati, S., & Walton, M. Scientific Reports, 12(1):21982, Nature Publishing Group UK London, dec, 2022. ![Digital restoration of colour cinematic films using imaging spectroscopy and machine learning [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Digital restoration is a rapidly growing methodology within the field of heritage conservation, especially for early cinematic films which have intrinsically unstable dye colourants that suffer from irreversible colour fading. Although numerous techniques to restore film digitally have emerged recently, complex degradation remains a challenging problem. This paper proposes a novel vector quantization (VQ) algorithm for restoring movie frames based on the acquisition of spectroscopic data with a custom-made push-broom VNIR hyperspectral camera (380–780 nm). The VQ algorithm utilizes what we call a multi-codebook that correlates degraded areas with corresponding non-degraded ones selected from reference frames. The spectral-codebook was compared with a professional commercially available film restoration software (DaVinci Resolve 17) tested both on RGB and on hyperspectral providing better results in terms of colour reconstruction.
@article{liu2022digital,
abstract = {Digital restoration is a rapidly growing methodology within the field of heritage conservation, especially for early cinematic films which have intrinsically unstable dye colourants that suffer from irreversible colour fading. Although numerous techniques to restore film digitally have emerged recently, complex degradation remains a challenging problem. This paper proposes a novel vector quantization (VQ) algorithm for restoring movie frames based on the acquisition of spectroscopic data with a custom-made push-broom VNIR hyperspectral camera (380–780 nm). The VQ algorithm utilizes what we call a multi-codebook that correlates degraded areas with corresponding non-degraded ones selected from reference frames. The spectral-codebook was compared with a professional commercially available film restoration software (DaVinci Resolve 17) tested both on RGB and on hyperspectral providing better results in terms of colour reconstruction.},
author = {Liu, L. and Catelli, E. and Katsaggelos, A. and Sciutto, G. and Mazzeo, R. and Milanic, M. and Stergar, J. and Prati, S. and Walton, M.},
doi = {10.1038/s41598-022-25248-5},
issn = {2045-2322},
journal = {Scientific Reports},
month = {dec},
number = {1},
pages = {21982},
pmid = {36539479},
publisher = {Nature Publishing Group UK London},
title = {{Digital restoration of colour cinematic films using imaging spectroscopy and machine learning}},
url = {https://www.nature.com/articles/s41598-022-25248-5},
volume = {12},
year = {2022}
}
Downloads: 0
{"_id":"GaLquCeCEMu6WghxL","bibbaseid":"liu-catelli-katsaggelos-sciutto-mazzeo-milanic-stergar-prati-etal-digitalrestorationofcolourcinematicfilmsusingimagingspectroscopyandmachinelearning-2022","author_short":["Liu, L.","Catelli, E.","Katsaggelos, A.","Sciutto, G.","Mazzeo, R.","Milanic, M.","Stergar, J.","Prati, S.","Walton, M."],"bibdata":{"bibtype":"article","type":"article","abstract":"Digital restoration is a rapidly growing methodology within the field of heritage conservation, especially for early cinematic films which have intrinsically unstable dye colourants that suffer from irreversible colour fading. Although numerous techniques to restore film digitally have emerged recently, complex degradation remains a challenging problem. This paper proposes a novel vector quantization (VQ) algorithm for restoring movie frames based on the acquisition of spectroscopic data with a custom-made push-broom VNIR hyperspectral camera (380–780 nm). The VQ algorithm utilizes what we call a multi-codebook that correlates degraded areas with corresponding non-degraded ones selected from reference frames. The spectral-codebook was compared with a professional commercially available film restoration software (DaVinci Resolve 17) tested both on RGB and on hyperspectral providing better results in terms of colour reconstruction.","author":[{"propositions":[],"lastnames":["Liu"],"firstnames":["L."],"suffixes":[]},{"propositions":[],"lastnames":["Catelli"],"firstnames":["E."],"suffixes":[]},{"propositions":[],"lastnames":["Katsaggelos"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Sciutto"],"firstnames":["G."],"suffixes":[]},{"propositions":[],"lastnames":["Mazzeo"],"firstnames":["R."],"suffixes":[]},{"propositions":[],"lastnames":["Milanic"],"firstnames":["M."],"suffixes":[]},{"propositions":[],"lastnames":["Stergar"],"firstnames":["J."],"suffixes":[]},{"propositions":[],"lastnames":["Prati"],"firstnames":["S."],"suffixes":[]},{"propositions":[],"lastnames":["Walton"],"firstnames":["M."],"suffixes":[]}],"doi":"10.1038/s41598-022-25248-5","issn":"2045-2322","journal":"Scientific Reports","month":"dec","number":"1","pages":"21982","pmid":"36539479","publisher":"Nature Publishing Group UK London","title":"Digital restoration of colour cinematic films using imaging spectroscopy and machine learning","url":"https://www.nature.com/articles/s41598-022-25248-5","volume":"12","year":"2022","bibtex":"@article{liu2022digital,\nabstract = {Digital restoration is a rapidly growing methodology within the field of heritage conservation, especially for early cinematic films which have intrinsically unstable dye colourants that suffer from irreversible colour fading. Although numerous techniques to restore film digitally have emerged recently, complex degradation remains a challenging problem. This paper proposes a novel vector quantization (VQ) algorithm for restoring movie frames based on the acquisition of spectroscopic data with a custom-made push-broom VNIR hyperspectral camera (380–780 nm). The VQ algorithm utilizes what we call a multi-codebook that correlates degraded areas with corresponding non-degraded ones selected from reference frames. The spectral-codebook was compared with a professional commercially available film restoration software (DaVinci Resolve 17) tested both on RGB and on hyperspectral providing better results in terms of colour reconstruction.},\nauthor = {Liu, L. and Catelli, E. and Katsaggelos, A. and Sciutto, G. and Mazzeo, R. and Milanic, M. and Stergar, J. and Prati, S. and Walton, M.},\ndoi = {10.1038/s41598-022-25248-5},\nissn = {2045-2322},\njournal = {Scientific Reports},\nmonth = {dec},\nnumber = {1},\npages = {21982},\npmid = {36539479},\npublisher = {Nature Publishing Group UK London},\ntitle = {{Digital restoration of colour cinematic films using imaging spectroscopy and machine learning}},\nurl = {https://www.nature.com/articles/s41598-022-25248-5},\nvolume = {12},\nyear = {2022}\n}\n","author_short":["Liu, L.","Catelli, E.","Katsaggelos, A.","Sciutto, G.","Mazzeo, R.","Milanic, M.","Stergar, J.","Prati, S.","Walton, M."],"key":"liu2022digital","id":"liu2022digital","bibbaseid":"liu-catelli-katsaggelos-sciutto-mazzeo-milanic-stergar-prati-etal-digitalrestorationofcolourcinematicfilmsusingimagingspectroscopyandmachinelearning-2022","role":"author","urls":{"Paper":"https://www.nature.com/articles/s41598-022-25248-5"},"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://sites.northwestern.edu/ivpl/files/2023/06/IVPL_Updated_publications-1.bib","dataSources":["ePKPjG8C6yvpk4mEK","ya2CyA73rpZseyrZ8","37Qfzv6wRptYkoSCL","zFPgsTDAW8aDnb5iN","E6Bth2QB5BYjBMZE7","nbnEjsN7MJhurAK9x","PNQZj6FjzoxxJk4Yi","7FpDWDGJ4KgpDiGfB","bod9ms4MQJHuJgPpp","QR9t5P2cLdJuzhfzK","D8k2SxfC5dKNRFgro","7Dwzbxq93HWrJEhT6","qhF8zxmGcJfvtdeAg","fvDEHD49E2ZRwE3fb","H7crv8NWhZup4d4by","DHqokWsryttGh7pJE","vRJd4wNg9HpoZSMHD","sYxQ6pxFgA59JRhxi","w2WahSbYrbcCKBDsC","XasdXLL99y5rygCmq","3gkSihZQRfAD2KBo3","t5XMbyZbtPBo4wBGS","bEpHM2CtrwW2qE8FP","teJzFLHexaz5AQW5z"],"keywords":[],"search_terms":["digital","restoration","colour","cinematic","films","using","imaging","spectroscopy","machine","learning","liu","catelli","katsaggelos","sciutto","mazzeo","milanic","stergar","prati","walton"],"title":"Digital restoration of colour cinematic films using imaging spectroscopy and machine learning","year":2022}