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Haiyong Liao

dblp:24/3610 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2011
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

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 deblurring
blind deconvolution		 0.112011
Blind Deconvolution Using Generalized Cross-Validation Approach to Regularization Parameter Estimation · IEEE Trans. Image Process. 2011
Image and video processing
image restoration		 0.112011
Blind Deconvolution Using Generalized Cross-Validation Approach to Regularization Parameter Estimation · IEEE Trans. Image Process. 2011
Image and video processing › regularization
regularization parameter estimation		 0.012011
Blind Deconvolution Using Generalized Cross-Validation Approach to Regularization Parameter Estimation · IEEE Trans. Image Process. 2011

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

total variation regularization · 0.1generalized cross-validation · 0.1alternating minimization · 0.1
YearPublicationVenuePosition
2011 Blind Deconvolution Using Generalized Cross-Validation Approach to Regularization Parameter Estimation
abstract
In this paper, we propose and present an algorithm for total variation (TV)-based blind deconvolution. Both the unknown image and blur can be estimated within an alternating minimization framework. With the generalized cross-validation (GCV) method, the regularization parameters associated with the unknown image and blur can be updated in alternating minimization steps. Experimental results confirm that the performance of the proposed algorithm is better than variational Bayesian blind deconvolution algorithms with Student's-t priors or a total variation prior.
Haiyong Liao, Michael Kwok-Po Ng
 IEEE Trans. Image Process.1