Automatic SAR image enhancement based on nonsubsampled contourlet transform and memetic algorithm

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.