An Investigation of Dehazing Effects on Image and Video Coding

By Kristofor B. Gibson, D. Vo, Truong Q. Nguyen. IEEE Transactions on Image Processing, 2012

305 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5b044e6b60978e7acaa992425e105a53374c7fbb

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In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Transmission Estimation in Underwater Single Images (2013, methodology): “The MDCP is constructed as described by Eq.”
Color Balance and Fusion for Underwater Image Enhancement (2018, methodology): “In contrast, applying DCP on our white balanced image version (last column), results in comparable and even better estimates compared with the specialized underwater techniques corresponding to UDCP [49], MDCP [50], BP [51] and Lu et al.”
Single Image Dehazing via Multi-scale Convolutional Neural Networks with Holistic Edges (2019, methodology): “Gibson et al. (2012) replace the time-consuming softmatting (He et al. 2011)with standard median filtering to improve computing efficiency.”
O-HAZE: A Dehazing Benchmark with Real Hazy and Haze-Free Outdoor Images (2018, unknown): “[17] employs median filter to avoid halo artifacts in the dehazed image.”
Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing (2014, methodology): “Therefore, single image haze removal has been a hot spot of research given its wider application range [5, 18, 8, 12, 19, 2, 7, 6].”
A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior (2015, unknown): “[31], Yu et al.”
Generalization of the Dark Channel Prior for Single Image Restoration (2018, background): “Since t̃rgb has block-like artifacts, it can be refined by median filtering [3], image matting [25], or guided filtering [26].”
Single Image Dehazing via Multi-scale Convolutional Neural Networks (2016, background): “Despite significant advances in this field, the state-of-the-art dehazing methods [2,11,29] are developed based on hand-crafted features.”
DehazeNet: An End-to-End System for Single Image Haze Removal (2016, methodology): “To improve computational efficiency, standard median filtering [12], median of median filter [13], guided joint bilateral filtering [14] and guided image filter [15] are used to replace the time-consuming soft matting [16].”