Blind Haze Separation

By Sarit Shwartz, Einav Namer, Yoav Y. Schechner. Computer Vision and Pattern Recognition, 2006

603 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/1f59f40be27ea17b40d5d54367624e89fc3b6659

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In Atomic and Molecular Physics, and Optics

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Underwater Image Enhancement by Wavelength Compensation and Dehazing (2012, background): “I. INTRODUCTION A CQUIRING clear images in underwater environmentsis an important issue in ocean engineering [1], [2].”
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].”
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.”
Efficient Image Dehazing with Boundary Constraint and Contextual Regularization (2013, neutral): “Representative works include [11], [9], [10], [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]”
Single image dehazing (2008, methodology): “As we mentioned in the beginning, independent component analysis is used in [Shwartz et al. 2006] to determine a certain parameter, the degree of polarization, that is required for polarization-based multi-image dehazing.”
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).”
Visibility in bad weather from a single image (2008, neutral): “The ﬁrst approach is to use polarizing ﬁlters (e.g. [10, 11]).”
Active Polarization Descattering (2009, background): “Polarization has also aided other computer vision aspects [2], [5], [7], [8], [27], [40], [43], [45], [54].”
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].”
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.”
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].”
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.”
Fast visibility restoration from a single color or gray level image (2009, methodology): “As a consequence, approaches based on the use of several images of the scene were proposed: using images at different times [8] or using images with different polarizing filters [11].”