TY - CHAP
T1 - Physically plausible dehazing for non-physical dehazing algorithms
AU - Vazquez-Corral, Javier
AU - Finlayson, Graham D.
AU - Bertalmío, Marcelo
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Images affected by haze usually present faded colours and loss of contrast, hindering the precision of methods devised for clear images. For this reason, image dehazing is a crucial pre-processing step for applications such as self-driving vehicles or tracking. Some of the most successful dehazing methods in the literature do not follow any physical model and are just based on either image enhancement or image fusion. In this paper, we present a procedure to allow these methods to accomplish the Koschmieder physical model, i.e., to force them to have a unique transmission for all the channels, instead of the per-channel transmission they obtain. Our method is based on coupling the results obtained for each of the three colour channels. It improves the results of the original methods both quantitatively using image metrics, and subjectively via a psychophysical test. It especially helps in terms of avoiding over-saturation and reducing colour artefacts, which are the most common complications faced by image dehazing methods.
AB - Images affected by haze usually present faded colours and loss of contrast, hindering the precision of methods devised for clear images. For this reason, image dehazing is a crucial pre-processing step for applications such as self-driving vehicles or tracking. Some of the most successful dehazing methods in the literature do not follow any physical model and are just based on either image enhancement or image fusion. In this paper, we present a procedure to allow these methods to accomplish the Koschmieder physical model, i.e., to force them to have a unique transmission for all the channels, instead of the per-channel transmission they obtain. Our method is based on coupling the results obtained for each of the three colour channels. It improves the results of the original methods both quantitatively using image metrics, and subjectively via a psychophysical test. It especially helps in terms of avoiding over-saturation and reducing colour artefacts, which are the most common complications faced by image dehazing methods.
KW - Colour image processing
KW - Image dehazing
KW - Image post-processing
UR - http://www.scopus.com/inward/record.url?scp=85064200109&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-13940-7_18
DO - 10.1007/978-3-030-13940-7_18
M3 - Chapter
AN - SCOPUS:85064200109
SN - 9783030139391
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 233
EP - 244
BT - Computational Color Imaging - 7th International Workshop, CCIW 2019, Proceedings
A2 - Horiuchi, Takahiko
A2 - Tominaga, Shoji
A2 - Trémeau, Alain
A2 - Schettini, Raimondo
ER -