Aiming at the problem that the traditional wavelet transform, curverlet transform and contourlet transform are unable to provide the optimal sparse representation of image and can not obtain the better enhancement effect, an image enhancement algorithm based on Shearlet transform was proposed. The image was decomposed into low frequency components and high frequency components by Shearlet transform. Firstly, Multi-Scale Retinex (MSR) was used to enhance the low frequency components of Shearlet decomposition to remove the effect of illumination on image; secondly, the threshold denoising was used to suppress noise at high frequency coefficients of each scale. Finally, the fuzzy contrast enhancement method was used to the reconstruction image to improve the overall contrast of image. The experimental results show that proposed algorithm can significantly improve the image visual effect, and it has more image texture details and anti-noise capabilities. The image definition, the entropy and the Peak Signal-to-Noise Ratio (PSNR) are improved to a certain extent compared with the Histogram Equalization (HE), MSR and Fuzzy contrast enhancement in Non-Subsampled Contourlet Domain (NSCT_fuzzy) algorithms. The operation time reduces to about one half of MSR and one tenth of NSCT_fuzzy.