Fast visibility restoration from a single color or gray level image

By Jean-Philippe Tarel, Nicolas Hautière. IEEE International Conference on Computer Vision, 2009

1,379 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7098bce6979dc00502f738746c7a6927a3c763d4

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In Computer Vision and Pattern Recognition

Citations in the corpus

Automatic Red-Channel underwater image restoration (2015, methodology): “Contrast and Visibility Recovery In this work, to evaluate the amount of contrast recovered we rely on a non-referenced metric proposed in (23) and used in (24) for purposes of visibility recovery assessment, on images corrupted by fog.”
Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging (2015, methodology): “[12], Tarel and Hautière [14], Ancuti and Ancuti [17], and the proposed method.”
Vision Enhancement in Homogeneous and Heterogeneous Fog (2012, methodology): “The visibility enhancement algorithm proposed in [1] is not optimized for road images.”
I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images (2018, background): “It has been addressed recently [7], [8], [9], [6], [10], [11], [12], [13], [14], [15], [16], [17], by considering different kinds of priors to estimate the transmission.”
O-HAZE: A Dehazing Benchmark with Real Hazy and Haze-Free Outdoor Images (2018, methodology): “Recently many strategies [15, 39, 20, 41, 25, 6, 5, 1, 16, 2, 14, 40] addressed this problem , by considering different kinds of priors to estimate the transmission.”
AOD-Net: All-in-One Dehazing Network (2017, methodology): “We compared the proposed model with several stateof-the-art dehazing methods: Fast Visibility Restoration (FVR) [25], Dark-Channel Prior (DCP) [8], Boundary Constrained Context Regularization (BCCR) [12], Automatic Atmospheric Light Recovery (ATM) [22], Color Attenuation Prior (CAP) [32], Non-local ”
Optimized contrast enhancement for real-time image and video dehazing (2013, methodology): “Tarel and Hautiere's algorithm [10] is computationally less complicated, but it changes color tones and exhibits halo artifacts .”
NH-HAZE: An Image Dehazing Benchmark with Non-Homogeneous Hazy and Haze-Free Images (2020, methodology): “Image dehazing is an ill-posed problem that has been solved initially based on additional information such as atmospheric cues [18, 32], multiple images captured with polarization filters [33, 37], or known depth information [25, 40].”
Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing (2018, background): “Single image dehazing approaches are divided into prior information-based methods [3,5,12,16,20,32] and learning based methods [10, 24,30,34,35].”
Color Balance and Fusion for Underwater Image Enhancement (2018, background): “coefficient as long as the model parameters are estimated locally [20], [21], [25].”
Semantic Foggy Scene Understanding with Synthetic Data (2017, positive): “For our synthesis of foggy images, we consider the standard optical model of [39], which is used extensively in the literature [20,29,54,63,64] and is formulated as where I ( x ) is the observed foggy image at pixel x , R ( x ) is the clear scene radiance and L is the atmospheric light.”
Enhancing underwater images and videos by fusion (2012, methodology): “We considered the specialized single underwater image enhancing techniques [5] but also the recent specialized dehazing technique [27].”
A Review of Computer Vision Techniques for the Analysis of Urban Traffic (2011, background): “However, there is a significant body of work that deals with image restoration under these conditions [10], [54], [134].”