Abstract

In order to overcome the problem in Retinex algorithm that it is possible to cause image detail loss by using Gauss function to estimate illumination, in this paper, we propose a color image enhancement algorithm based on multi-scale morphology unsharp method combined with Retinex. Firstly, the color image is converted from RGB space to HSV space. Then, the S component is enhanced by adaptive logarithmic transformation to improve its visual characteristics and make it consistent with human visual system. The method of multi-scale morphological unsharpening is adopted to estimate the V component, and the multi-scale weights are adaptively selected according to the features of the image. The V component is enhanced according to the Retinex principle. Finally, the H and the enhanced S and V components are recombined and reflected into RGB space to achieve the purpose of image enhancement. The experimental results showed that the algorithm is superior to the traditional SSR, MSR and MSRCR algorithms in terms of entropy, average gradient and sharpness, and outperforms those based on Gauss function estimation.

Details

Title
Color image enhancement based on adaptive multi-scale morphological unsharpening filter
Author
Liu, X 1 ; Xia, X H 1 ; Wang, L 2 ; Cao, J H 3 

 Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, China; Engineering training Centre, Wuhan University of Science and Technology, Wuhan 430065, China 
 Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, China; Center for Service Science and Engineering, Wuhan University of Science and Technology, Wuhan 430065, China 
 Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, China 
Publication year
2018
Publication date
Oct 2018
Publisher
IOP Publishing
ISSN
17578981
e-ISSN
1757899X
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2557137081
Copyright
© 2018. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.