TY - JOUR
T1 - Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset
AU - Berman, Dana
AU - Levy, Deborah
AU - Avidan, Shai
AU - Treibitz, Tali
N1 - Publisher Copyright:
© 1979-2012 IEEE.
PY - 2021/8/1
Y1 - 2021/8/1
N2 - Underwater images suffer from color distortion and low contrast, because light is attenuated while it propagates through water. Attenuation under water varies with wavelength, unlike terrestrial images where attenuation is assumed to be spectrally uniform. The attenuation depends both on the water body and the 3D structure of the scene, making color restoration difficult. Unlike existing single underwater image enhancement techniques, our method takes into account multiple spectral profiles of different water types. By estimating just two additional global parameters: the attenuation ratios of the blue-red and blue-green color channels, the problem is reduced to single image dehazing, where all color channels have the same attenuation coefficients. Since the water type is unknown, we evaluate different parameters out of an existing library of water types. Each type leads to a different restored image and the best result is automatically chosen based on color distribution. We also contribute a dataset of 57 images taken in different locations. To obtain ground truth, we placed multiple color charts in the scenes and calculated its 3D structure using stereo imaging. This dataset enables a rigorous quantitative evaluation of restoration algorithms on natural images for the first time.
AB - Underwater images suffer from color distortion and low contrast, because light is attenuated while it propagates through water. Attenuation under water varies with wavelength, unlike terrestrial images where attenuation is assumed to be spectrally uniform. The attenuation depends both on the water body and the 3D structure of the scene, making color restoration difficult. Unlike existing single underwater image enhancement techniques, our method takes into account multiple spectral profiles of different water types. By estimating just two additional global parameters: the attenuation ratios of the blue-red and blue-green color channels, the problem is reduced to single image dehazing, where all color channels have the same attenuation coefficients. Since the water type is unknown, we evaluate different parameters out of an existing library of water types. Each type leads to a different restored image and the best result is automatically chosen based on color distribution. We also contribute a dataset of 57 images taken in different locations. To obtain ground truth, we placed multiple color charts in the scenes and calculated its 3D structure using stereo imaging. This dataset enables a rigorous quantitative evaluation of restoration algorithms on natural images for the first time.
KW - Image processing and computer vision
KW - computational photography
KW - image color analysis
KW - image enhancement
KW - image restoration
UR - http://www.scopus.com/inward/record.url?scp=85111790333&partnerID=8YFLogxK
U2 - 10.1109/TPAMI.2020.2977624
DO - 10.1109/TPAMI.2020.2977624
M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???
C2 - 32142424
AN - SCOPUS:85111790333
SN - 0162-8828
VL - 43
SP - 2822
EP - 2837
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
JF - IEEE Transactions on Pattern Analysis and Machine Intelligence
IS - 8
M1 - 9020130
ER -