Single Image Haze Removal Using Dark Channel Prior

By Kaiming He, Jian‐Yuan Sun, Xiaoou Tang. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011

1,722 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/3360c3ccdf73539200491fe958295302fef04d6e

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Citations in the corpus

GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing (2019, methodology): “Representative works along this line of research include [36, 5, 9, 37, 42].”
Image Deblurring via Extreme Channels Prior (2017, methodology): “[6] based on the observation that in most of the natural scene patches, at least one of the color channels possesses some pixels with very close to zero intensities.”
Fast and memory efficient de-hazing technique for real-time computer vision applications (2020, methodology): “He et al. [4] proposed a dark-channel method (DCM) with refined transmission depth using an edge-preserving filter called guided filter [5].”
Artificial intelligence in the creative industries: a review (2020, methodology): “ight and t is medium transmission. The transmission t is estimated using a dark channel prior based on the observation that the lowest value of each color channel of haze-free images is close to zero [266]. In [267], the true colors are recovered based on the assumption that an image can be faithful”
Color Balance and Fusion for Underwater Image Enhancement (2018, methodology): “Recently, several algorithms that specifically restore underwater images based on Dark Channel Prior (DCP) [19], [26] have been introduced.”
Underwater Image Enhancement Using a Multiscale Dense Generative Adversarial Network (2020, methodology): “The dark channel prior method [23] assumes that, in most local patches of haze-free outdoor images, there will be some pixels with very low intensities in at least one color channel.”
Removal of water scattering (2010, methodology): “BACKGROUND: COLORS AND CONTRAST IN WATER First we are going to look at the dichromatic scattering model in air [5] which states that the color of a scene point e in fog or haze observed by a color camera, is given by a linear combination of the direction a of airlight (fog or haze) color, and the…”
Transmission Estimation in Underwater Single Images (2013, methodology): “After simplifications and following the nomenclature used in [9, 11], the direct component is defined as:”
Enhanced Pix2pix Dehazing Network (2019, background): “[7] [8] proposed a dark channel prior for the estimation of the transmission map.”
Emerging From Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfer (2017, background): “image restoration methods [7]–[13] are inspired by the outdoor dehazing strategies [19]–[22].”
“Double-DIP”: Unsupervised Image Decomposition via Coupled Deep-Image-Priors (2018, methodology): “He [23] Meng [28] Fattal [18] Cai [10] Ancuti [3] Berman [8] Ren [30] Bahat [6] Ours PSNR 16.”
Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather (2019, methodology): “These priors are either hand-crafted [25] and used for depth and transmission estimation separately, or they are learned jointly as part of trainable end-to-end models [38, 32, 73].”
Low-Light Image Enhancement With Semi-Decoupled Decomposition (2020, methodology): “15, the results of [41] have strong artifacts, while our method removes most of the haze and keeps the natural visual appearance.”