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Keming Cao

dblp:217/4646 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Image and video processing · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video processing › image restoration › image dehazing
dark channel prior
0.312018
Generalization of the Dark Channel Prior for Single Image Restoration · IEEE Trans. Image Process. 2018
Image and video processing › image restoration
image dehazing
0.312018
Generalization of the Dark Channel Prior for Single Image Restoration · IEEE Trans. Image Process. 2018
Image and video processing
image restoration
0.312018
Generalization of the Dark Channel Prior for Single Image Restoration · IEEE Trans. Image Process. 2018

Methods — techniques the papers use, named apart from their topics

image formation model · 0.3ambient light estimation · 0.3adaptive color correction · 0.3
YearPublicationVenuePosition
2018 Generalization of the Dark Channel Prior for Single Image Restoration
abstract
Images degraded by light scattering and absorption, such as hazy, sandstorm, and underwater images, often suffer color distortion and low contrast because of light traveling through turbid media. In order to enhance and restore such images, we first estimate ambient light using the depth-dependent color change. Then, via calculating the difference between the observed intensity and the ambient light, which we call the scene ambient light differential, scene transmission can be estimated. Additionally, adaptive color correction is incorporated into the image formation model (IFM) for removing color casts while restoring contrast. Experimental results on various degraded images demonstrate the new method outperforms other IFM-based methods subjectively and objectively. Our approach can be interpreted as a generalization of the common dark channel prior (DCP) approach to image restoration, and our method reduces to several DCP variants for different special cases of ambient lighting and turbid medium conditions.
Yan-Tsung Peng, Keming Cao, Pamela C. Cosman
IEEE Trans. Image Process.2