A foggy image sharpening algorithm based on nonsubsampled Contourlet transformation (NSCT) is proposed. The foggy image is mapped to the HIS color space, and the luminance component H and the saturation component S are processed respectively. The NSCT is used to process the luminance component H. The low-frequency component containing most energy is negated and then processed by the improved single-scale Retinex algorithm. The image is negated again and is superposed linearly with the low-frequency components processed by the improved single-scale Retinex algorithm directly. A fast bilateral-filter is applied to the high-frequency components that contain most of the linear details of the image. Then the two processed components are inversely transformed by NSCT, and the processed luminance components are obtained. Finally, the saturation component S is linearly stretched to achieve color compensation. The processed image of each component is mapped backward to the RGB color space to get a clear foggy image. The experimental results show that the proposed algorithm achieves good results of the details and color fidelity for the foggy image. Compared with other algorithms, the standard deviation, information entropy and peak signal to noise ratio are improved.