Improving flexibility and efficiency by adding parallelism to genetic algorithms
Statistics and Computing
Reconstruction of Wavelet Coefficients Using Total Variation Minimization
SIAM Journal on Scientific Computing
Special issue on emerging trends in soft computing: memetic algorithms
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Emerging Trends in Soft Computing - Memetic Algorithms; Guest Editors: Yew-Soon Ong, Meng-Hiot Lim, Ferrante Neri, Hisao Ishibuchi
A filter bank for the directional decomposition of images: theoryand design
IEEE Transactions on Signal Processing
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Image enhancement based on a nonlinear multiscale method
IEEE Transactions on Image Processing
Gray and color image contrast enhancement by the curvelet transform
IEEE Transactions on Image Processing
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
The Nonsubsampled Contourlet Transform: Theory, Design, and Applications
IEEE Transactions on Image Processing
Translation-Invariant Contourlet Transform and Its Application to Image Denoising
IEEE Transactions on Image Processing
Combined Curvelet Shrinkage and Nonlinear Anisotropic Diffusion
IEEE Transactions on Image Processing
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This paper presents an automatic enhancement method for SAR images based on the nonsubsampled contourlet transform (NSCT) and the memetic algorithm (MA). Firstly, an improved enhancement function which integrates the speckle reduction with the feature enhancement is proposed to nonlinearly shrink and stretch the NSCT coefficients, and then an multi-population cooperative MA (MP-CMA) is presented to automatically adjust the parameters of the enhancement function. We propose an objective criterion for enhancement, and attempt finding the (near) optimal image according to the enhancement criterion. We employ the MP-CMA as a global search strategy for the best enhancement image which has a satisfactory compromise between sharpening and smoothing. The experimental results show that the proposed method can efficiently enhance the edge features and contrast of SAR images and reduce the speckle noises and outperforms the wavelet-based and NSCT-based non-automatic enhancement methods.