Single image dehazing

By Raanan Fattal. ACM Transactions on Graphics, 2008

2,406 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ed4683971a9a8adc18282ce47bd593b896cb5c28

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In Computer Vision and Pattern Recognition

Citations in the corpus

PSD: Principled Synthetic-to-Real Dehazing Guided by Physical Priors (2021, background): “9–11 are from authors of previous work [4–6].”
Enhancement of Underwater Images With Statistical Model of Background Light and Optimization of Transmission Map (2019, methodology): “A simplified image formation model (IFM) [20], [29]–[32] is often used to approximate the propagation equation of underwater scattering in the background light, can be shown as:”
Non-local Image Dehazing (2016, methodology): “Finding Haze-Lines: We estimate A using one of the previous methods [2, 5, 18].”
An Underwater Color Image Quality Evaluation Metric (2015, methodology): “based algorithms [36], [37], contrast stretching methods [38], [39] and the newest image fusion enhancement [6].”
O-HAZE: A Dehazing Benchmark with Real Hazy and Haze-Free Outdoor Images (2018, background): “The other columns, from left to right, present the results yielded by the techniques of He et al. [19], Meng et al. [26], Fattal [16], Cai et al. [8], Ancuti et al. [3], Berman et al. [7] and Ren et al. [32].”
Initial results in underwater single image dehazing (2010, background): “6 contains the comparison with Fattal’s work for two underwater images.”
Weighted Guided Image Filtering (2016, methodology): “(a, h) input images; (b, i) de-hazed images by the algorithm in [34]; (c, j) de-hazed images by the algorithm in [27]; (d, k) de-hazed images by the algorithm in [33]; (e, l) de-hazed images by the algorithm in [7]; (f, m) de-hazed images by the algorithm in [14]; and (g, n) de-hazed images by the p”
Benchmarking Single-Image Dehazing and Beyond (2017, background): “We appreciate the support from the authors of [14], [18].”
Optimized contrast enhancement for real-time image and video dehazing (2013, methodology): “Fattal [8] decomposed the scene radiance of an image into the albedo and the shading, and then estimated the scene radiance based on independent component analysis (ICA), assuming that the shading and the object depth are locally uncorrelated.”
Transmission Estimation in Underwater Single Images (2013, methodology): “Recently, several dehazing algorithms using a single image have been proposed [6, 20, 9].”
SoilingNet: Soiling Detection on Automotive Surround-View Cameras (2019, neutral): “An alternate approach is to image restoration to improve the quality of the image, for example Dehazing approaches were proposed in [3], [1] and [9].”
D-HAZY: A dataset to evaluate quantitatively dehazing algorithms (2016, background): “While earlier dehazing approaches employ additional information such as multiple images [3] or a rough estimate of the depth [4], recent techniques have tackled this problem by using only the information of a single hazy input image [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16].”
Underwater Image Enhancement by Dehazing With Minimum Information Loss and Histogram Distribution Prior (2016, neutral): “Fattal [10] exploited the fact that the surface shading and transmission functions are locally statistically uncorrelated.”
Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging (2015, methodology): “Next, we compared the defogged images obtained using the models of Tan [10], Fattal [11], Kopf [8], He et al.”
Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing (2014, methodology): “Fattal [5] assumes the transmission and surface shading are locally uncorrelated and estimate the haze by independent component analysis.”
Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding (2018, background): “Extensive research has been conducted on image defogging (dehazing) to increase the visibility of foggy scenes [5,15,16,24,36,40,52].”
FFA-Net: Feature Fusion Attention Network for Single Image Dehazing (2019, methodology): “(Fattal 2008) present a new method for estimating the optical transmission in hazy scenes, the scattered light is eliminated to increase scene visibility and recover haze-free scene contrasts., (Berman, Avidan, and others 2016) proposed a non-local prior to characterize the clean image, the…”
Fast visibility restoration from a single color or gray level image (2009, methodology): “6 shows a comparison between results obtained by [1] and our algorithm.”