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Dongbin Shi

dblp:72/5823 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2007
—ORCID · none

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 enhancement › contrast enhancement
histogram specification
0.112007
Joint Exact Histogram Specification and Image Enhancement Through the Wavelet Transform · IEEE Trans. Image Process. 2007
Image and video processing
image enhancement
0.112007
Joint Exact Histogram Specification and Image Enhancement Through the Wavelet Transform · IEEE Trans. Image Process. 2007
Image and video processing
wavelet transform
0.012007
Joint Exact Histogram Specification and Image Enhancement Through the Wavelet Transform · IEEE Trans. Image Process. 2007

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

wavelet transform · 0.1pixel ordering · 0.1
YearPublicationVenuePosition
2007 Joint Exact Histogram Specification and Image Enhancement Through the Wavelet Transform
abstract
Histogram specification (equalization) is an important tool for tasks such as image enhancement and normalization. Although this problem has exact solution for continuous images, in the case of digital images, it is ill posed. Recently, an exact pixel ordering method based solely on local image intensity was proposed, yet still with some limitations, especially since it ignores the important image edge information. In this paper, we present a wavelet-based method that simultaneously achieves the exact histogram specification and good image enhancement performance. It does so through a carefully designed strict pixel ordering process, during which the wavelet coefficients are fine tuned for the image enhancement purpose. Compared to previous work, this approach takes into account not only local mean intensity values, but also local edge information. Other advantages include fast pixel ordering, good statistical models, and better image enhancement performance. Experimental results and comparison with state-of-the-art methods are presented.
Yi Wan 0003, Dongbin Shi
IEEE Trans. Image Process.2