An adaptive image enhancement method based on contourlet transform and improved ant colony algorithm

Authors:
Lei Li;Qiqing Guo;Dandan Gu
Affiliations:
Department of Computer Engineering, Henan Polytechnic Institute, Nanyang, China;Department of Computer Engineering, Henan Polytechnic Institute, Nanyang, China;Electronic and Information Engineering, Beihang University, Beijing, China
Venue:
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2011

Citing 5
Cited 0

Study on Immunized Ant Colony Optimization

ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
Multiresolution image representation using combined 2-D and 1-D directional filter banks

IEEE Transactions on Image Processing
A filter bank for the directional decomposition of images: theoryand design

IEEE Transactions on Signal Processing
The contourlet transform: an efficient directional multiresolution image representation

IEEE Transactions on Image Processing
Translation-Invariant Contourlet Transform and Its Application to Image Denoising

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

To overcome the problems of losing detail geometric information of images and tending to amplify noise, which exist in traditional image enhancement methods, an adaptive image enhancement method based on Contourlet transform and improved ant colony algorithm is proposed. Firstly, we obtain the coefficients in different scales and different directions by image decomposition using the Contourlet transform. Then, we adopt an adaptive enhancement function with the ability of both feature enhancement and noise reduction to modify the Contourlet coefficients nonlinearly, and use an improved ant colony algorithm to adaptively adjust the parameters of the enhancement function. To find the optimal parameters, a novel evaluation criterion for image enhancement is introduced. Finally, we obtain the enhanced image by Contourlet inverse transform. The experimental results show that our method obtains significant performance in feature enhancement with low contrast and noise reduction over the wavelet-based and Contourlet-based non-adaptive image enhancement methods.