Polarization-based vision through haze.

By Yoav Y. Schechner, Srinivasa G. Narasimhan, Shree K. Nayar. Applied Optics, 2008

419 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/c4158963f7adfb375279ddb58585086d2d4eb101

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images (2018, background): “Oldest approaches rely on atmospheric cues [1], [2], multiple images captured with polarization ﬁlters [3], [4], or known depth [5], [6].”
Bayesian Defogging (2012, methodology): “The method by Schechner et al. (2003) uses the polarization characteristics of scene reflection and airlight to physically remove their attenuation and transmittance, thus we may assume that their results represent the true clear-day scene.”
Dense-Haze: A Benchmark for Image Dehazing with Dense-Haze and Haze-Free Images (2019, methodology): “To solve this problem, earlier techniques employ polarization ﬁlters [1], [2], and depth knowledge prior [3], [4].”
Flash Photography for Data-Driven Hidden Scene Recovery (2018, background): “NLOS imaging has been explored widely in the context of vision related applications such as: image dehazing [1, 5, 9, 32], seeing through translucent objects [10], cloud tomography [16, 27], underwater imaging [33], and tracking [12, 19].”
Active Polarization Descattering (2009, background): “Some assumed a negligible degree of polarization (DOP) of the objects [38], [39], [41], [50].”
NH-HAZE: An Image Dehazing Benchmark with Non-Homogeneous Hazy and Haze-Free Images (2020, background): “This is an ill-posed problem that has been solved initially using additional information [18, 37].”
O-HAZE: A Dehazing Benchmark with Real Hazy and Haze-Free Outdoor Images (2018, methodology): “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.”
Single Image Dehazing by Multi-Scale Fusion (2013, background): “Another class of techniques exploits the properties of the airlight that is partially polarized [5], [20]–[23].”
Deep photo: model-based photograph enhancement and viewing (2008, methodology): “Some haze removal techniques make use of multiple images; e.g., images taken under different weather conditions [Narasimhan and Nayar 2003a], or with different polarizer orientations [Schechner et al. 2003].”
Blind Haze Separation (2006, background): “1 In this approach, the required parameters have been derived [25] from measurements of pixels that correspond to sky by the horizon (even automatically [17]).”