Color image enhancement based on adaptive multi-scale morphological unsharpening filter

4th International Conference on Applied Materials and Manufacturing Technology

材料科学;机械制造

Liu, X.^1,2 ; Xia, X.H.^1,2 ; Wang, L.^1,3 ; Cao, J.H.^1

Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan

430081, China^1

Engineering Training Centre, Wuhan University of Science and Technology, Wuhan

430065, China^2

Center for Service Science and Engineering, Wuhan University of Science and Technology, Wuhan

430065, China^3

关键词: Average gradient;    Color image enhancement;    Gauss function;    Human Visual System;    Image details;    Logarithmic transformations;    Multi scale morphology;    Retinex algorithms;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/423/1/012082/pdf
DOI  :  10.1088/1757-899X/423/1/012082

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

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.