Aiming at the problem of contrast deficiency and low luminance in some remote sensing images and hyperspectral images, an enhancement method in non-subsampled contourlet transform (NSCT) domain based on multi-scale Retinex (MSR) and niche chaotic mutation particle swarm optimization (NCPSO) is proposed to improve the quality of images. Firstly, an image is decomposed through NSCT. A low-frequency component and several high-frequency components in different directions are produced. Then the low-frequency component is enhanced by the multi-scale Retinex algorithm with hybrid intensity transfer function. While the coefficients of high-frequency components are adjusted to enhance the edges by nonlinear gain function. The optimal parameters in the nonlinear gain function are searched by the niche chaotic particle swarm optimization algorithm, whose fitness is the integrated quantitative evaluation function considering both contrast and information entropy. A large number of experimental results show that, compared with four enhancement methods such as histogram double equalization method, non-subsampled contourlet transform method, multi-scale Retinex method and stationary wavelet transform and Retinex method, the proposed method can improve the contrast and information entropy more efficiently, and enhances the whole visual effects.