Recovery of underwater visibility and structure by polarization analysis

By Yoav Y. Schechner, Nir Karpel. IEEE Journal of Oceanic Engineering, 2005

643 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/d73c16209f6802140b16d4f6fa960705f7cc77e4

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In Atomic and Molecular Physics, and Optics

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Citations in the corpus

Underwater Image Restoration Based on Image Blurriness and Light Absorption (2017, background): “Also, scattered background light coming from different colors of water is blended with the scene radiance along the light of sight [3], resulting in underwater scenes often having low contrast and color distortions.”
Active Polarization Descattering (2009, methodology): “Some recovered visibility as well as the 3D structure in haze and underwater [38], [39], [40] under distant natural illumination.”
Underwater Image Processing: State of the Art of Restoration and Image Enhancement Methods (2010, background): “Image courtesy of Schechner and Karpel [17].”
A Revised Underwater Image Formation Model (2018, methodology): “Current underwater color correction methods [5, 8, 9, 12, 13, 26, 27, 31, 36, 40] rely on a commonly used image formation model first derived for haze [3, 4, 14, 17, 39].”
Automatic Red-Channel underwater image restoration (2015, methodology): “(6), (8), (10), (12), and the edge maps computed by the evaluation metric we are using are given in (7), (9), (11), (13).”
An Underwater Color Image Quality Evaluation Metric (2015, background): “Schechner and Karpel [25] applied global contrast as a measure of underwater image quality.”
I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images (2018, background): “ion. Other approaches rely on the registration of geometric models to get access to image depth [22]. Hardware-based solutions have also been considered, such as the ones based on polarization ﬁlters [11,35,34], which capture multiple images of the same scene with different degrees of polarization (”
O-HAZE: A Dehazing Benchmark with Real Hazy and Haze-Free Outdoor Images (2018, background): “Hardware-based solutions have also been considered, such as the ones based on polarization filters [11, 37, 36], which capture multiple images of the same scene with different degrees of polarization (DOP) and compare them to estimate the haze properties.”
Blind Haze Separation (2006, background): “Moreover, research into atmospheric imaging promotes other domains of vision through scattering media, such as water [9, 11, 14, 23, 24, 31] and tissue.”
Sea-Thru: A Method for Removing Water From Underwater Images (2019, methodology): “This model then became extensively used for bad weather, and later adapted for the underwater environment [52].”
Human-Visual-System-Inspired Underwater Image Quality Measures (2016, methodology): “Some generic image quality measures, such as the receiver operating characteristic (ROC) curve analysis [6], the histogram analysis [9], [10], the Michelson contrast measure [11], Hasler’s colorfulness measure [12], and color image quality measure (CIQI) [13] have been developed.”
Underwater Image Enhancement by Wavelength Compensation and Dehazing (2012, background): “Techniques targeting on removal of light scattering distortion include exploiting the polarization effects to compensate for visibility degradation [7], using image dehazing to restore the clarity of the underwater images [8], and combining point spread functions and a modulation transfer function t”
Underwater image enhancement via extended multi-scale Retinex (2017, neutral): “og [ S(x, y )] = log[ L (x, y )] + log[ R (x, y )] (3)”
Transmission Estimation in Underwater Single Images (2013, methodology): “In this case, the attenuation coefficient η and the airlight A was defined as proposed in [14] and [19], respectively.”