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Optical Engineering

Retinex image enhancement via a learned dictionary
Author(s): Huibin Chang; Michael K. Ng; Wei Wang; Tieyong Zeng
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Paper Abstract

The main aim of this paper is to study image enhancement by using sparse and redundant representations of the reflectance component in the Retinex model over a learned dictionary. This approach is different from existing variational methods, and the advantage of this approach is that the reflectance component in the Retinex model can be represented with more details by the dictionary. A variational method based on the dynamic dictionaries is adopted here, where it changes with respect to iterations of the enhancement algorithm. Numerical examples are also reported to demonstrate that the proposed methods can provide better visual quality of the enhanced high-contrast images than the other variational methods, i.e., revealing more details in the low-light part.

Paper Details

Date Published: 26 January 2015
PDF: 15 pages
Opt. Eng. 54(1) 013107 doi: 10.1117/1.OE.54.1.013107
Published in: Optical Engineering Volume 54, Issue 1
Show Author Affiliations
Huibin Chang, Tianjin Normal Univ. (China)
Michael K. Ng, Hong Kong Baptist Univ. (Hong Kong)
Wei Wang, Tongji Univ. (China)
Tieyong Zeng, Hong Kong Baptist Univ. (Hong Kong)

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