Visibility in bad weather from a single image

By Robby T. Tan. 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008

2,397 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/1deb84164223a3468a80b376befc7e0b7261a36b

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In Atomic and Molecular Physics, and Optics

In Computer Vision and Pattern Recognition

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Citations in the corpus

Gated Context Aggregation Network for Image Dehazing and Deraining (2018, methodology): “[34] use Markov Random Field to maximize the lo-cal contrast of an image by assuming that the local contrast of a clear image is higher than that of a hazy image.”
Underwater Image Enhancement by Wavelength Compensation and Dehazing (2012, methodology): “If an artificial light source is detected, the luminance introduced by the auxiliary lighting is removed from the foreground area to avoid overcompensation in the stages followed.”
Single Image Dehazing by Multi-Scale Fusion (2013, methodology): “as can be noticed by a closed inspection of figure 8 of [7], in figure 1 of [10] or in figure 6 of [14]).”
Gated Fusion Network for Single Image Dehazing (2018, background): “[33] enhanced the visibility of hazy images by maximizing the contrast.”
Contrastive Learning for Compact Single Image Dehazing (2021, methodology): “Single image dehazing aims to generate the haze-free images from the hazy observation images, which can be categorized into prior-based methods [45, 17, 53, 4] and learning-based methods [5, 25, 51, 35, 23].”
Densely Connected Pyramid Dehazing Network (2018, background): “Apart from several works that focus on estimating the atmospheric light [4, 40], most of the other algorithms concentrate more on the accurate estimation of the transmission map and they leverage empirical rule in estimating the atmospheric light [13, 29, 33, 41].”
DesnowNet: Context-Aware Deep Network for Snow Removal (2017, methodology): “So far, approaches for dealing with haze particles have been designed with a strong assumption [11, 29], attenuation prior [4–6, 15, 17–19] and feature learning [3], based on the observation that haze is uniformly accumulated over an entire image.”
GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing (2019, methodology): “Representative works along this line of research include [36, 5, 9, 37, 42].”
Single Image Dehazing via Multi-scale Convolutional Neural Networks with Holistic Edges (2019, methodology): “2011), textural and contrast (Tan 2008) properties of hazy images to determine the transmission in the scenes.”
Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing (2014, methodology): “Based on this observation, Tan’s method [18] enhances the visibility of the image by maximizing the local contrast.”
Optimized contrast enhancement for real-time image and video dehazing (2013, background): “Tan [7] maximized the contrast of a hazy image, assuming that a haze-free image has a higher contrast ratio than the hazy image.”
NH-HAZE: An Image Dehazing Benchmark with Non-Homogeneous Hazy and Haze-Free Images (2020, methodology): “Another direction of research in image dehazing includes those methods that restore the visibility of hazy image without assuming the optical model [38, 1, 17].”
Single Image Dehazing via Conditional Generative Adversarial Network (2018, methodology): “The adaptive color contrast enhancement-based method usually suffers from visual artifacts such as color blocking and aliasing, which are invisible in the input and obvious in the output [6, 33].”
A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior (2015, methodology): “Although Tan’s approach is able to achieve impressive results, it tends to produce over-saturated images.”
Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding (2021, background): “According to the image formation model in bad weather [47], [48], which is widely used in image dehazing and underwater image restoration algorithms [2], [29], [30], the quality-degraded image can be expressed as: where x indicates the pixel index, I is the observed image, J is the clear image, A…”
Sea-Thru: A Method for Removing Water From Underwater Images (2019, methodology): “This was done to enable recovery from single images in haze [8, 26, 33, 57] and later used also for underwater recovery [17, 19, 21, 44, 49].”