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.
|Title of host publication||Computational Color Imaging|
|Subtitle of host publication||7th International Workshop, CCIW 2019, Chiba, Japan, March 27-29, 2019, Proceedings|
|Editors||Shoji Tominaga, Raimondo Schettini, Alain Trémeau, Takahiko Horiuchi|
|Publication status||Published - 20 Feb 2019|
|Name|| Lecture Notes in Computer Science|