Retinex image enhancement via a learned dictionary

Huibin Chang*, Kwok Po NG, Wei Wang, Tieyong ZENG

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

36 Citations (Scopus)


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.

Original languageEnglish
Article number140914
JournalOptical Engineering
Issue number1
Publication statusPublished - 1 Jan 2015

Scopus Subject Areas

  • Atomic and Molecular Physics, and Optics
  • Engineering(all)

User-Defined Keywords

  • Image enhancement
  • Learned dictionaries
  • Retinex
  • Sparse and redundant representations
  • Total variation


Dive into the research topics of 'Retinex image enhancement via a learned dictionary'. Together they form a unique fingerprint.

Cite this