Instant dehazing of images using polarization

By Yoav Y. Schechner, Srinivasa G. Narasimhan, Shree K. Nayar. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 2001

950 citations recorded by the source corpus.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/da8a5b3e01ed2a65c0ae5664c1c96e4cee22b843

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Citations in the corpus

Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing (2014, methodology): “Polarized filters are used in [15, 16] to capture multiple images of the same scene, and then different degrees of polarization (DOP) of images are used for haze removal.”
Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging (2015, background): “Multiple foggy images of the same scene [2] or obtained by different degrees of polarization by rotating polarizing filters attached to a camera [4] have also been used.”
Single Image Dehazing via Multi-scale Convolutional Neural Networks with Holistic Edges (2019, background): “often require multiple frames to deal with this problem (Treibitz and Schechner 2009; Narasimhan and Nayar 2003, 2000; Schechner et al. 2001; Shwartz et al. 2006).”
Single Image Haze Removal Using Dark Channel Prior (2011, methodology): “Polarizationbased methods [3], [4] remove the haze effect through two or more images taken with different degrees of polarization.”
Single image haze removal using dark channel prior (2009, methodology): “Polarization based methods [14, 15] remove the haze effect through two or more images taken with different degrees of polarization.”
GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing (2019, background): “Early works on image dehazing either require multiple images of the same scene taken under different conditions [30, 32, 20, 22, 24] or side information acquired from other sources [23, 12].”
Initial results in underwater single image dehazing (2010, background): “In order to resolve the ambiguity between the depth and the direct transmission, many existing dehazing techniques rely on additional hardware such as polarization filters [3, 4], additional information such as the depth of the scene [5], or multiple images captured under different conditions [2, 6]”
Active Polarization Descattering (2009, background): “Some recovered visibility as well as the 3D structure in haze and underwater [38], [39], [40] under distant natural illumination.”
Optimized contrast enhancement for real-time image and video dehazing (2013, background): “For example, object depths are estimated from two images, which are captured in different weather conditions [2] [3] or with different degrees of polarization [4] [5].”
Non-local Image Dehazing (2016, methodology): “Generally, β is wavelength dependent and therefore t is different per color channel [11, 16].”
Benchmarking Single-Image Dehazing and Beyond (2017, background): “While some earlier works consider multiple images from the same scene to be available for dehazing [2], [3], [4], [5], the single image dehazing proves to be a more realistic setting in practice, and thus gained the dominant popularity.”
Gated Fusion Network for Single Image Dehazing (2018, background): “Some approaches try to use visual cues to capture deterministic and statistical properties of hazy images [3, 6, 8, 30].”
Visibility in bad weather from a single image (2008, background): “= D(x)eγ c(x) +A(x) (10) whereγ c is the normalized object chromaticity.”
Single Image Dehazing via Multi-scale Convolutional Neural Networks (2016, background): “As image dehazing is ill-posed, early approaches often require multiple images to deal with this problem [17,18,19,20,21,22].”