Ａ super-resolution algorithm based on TV-L1 decomposition was proposed . In the algorithm, the original-dual algorithm was used to solve the TV-L1 image decomposition model, and the low resolution image was decomposed into the structure and the texture parts. The structure part was processed with a soft decision adaptive interpolation. For the texture part, the Nonsubsampled Contourlet Transform (NSCT) characterized by multi-direction and shift-invariance was used to construct the nonlinear gain function to process the NSCT transform domain coefficients, then the processed transform coefficients were enhanced their textures by the NSCT inverse transform. Finally, the reconstructed high resolution image was obtained by combining the processed the structure and texture parts. Experimental results show that the proposed algorithm in both visual effect and the quantitative evaluation on image quality is better than the traditional interpolation method. For realizing twice super-resolution, its peak signal-to-noise ratio (PSNR) and Structural Similarity (SSIM) average value are increased by 1.316 2-4.591 9 dB and 0.007 1-0.020 6. For realizing three super-resolution, the PSNR and the SSIM are increased by 0.338 7-4.58 0 dB and 0.001 8-0.041 7, respectively. Because of the accurate representation of the different morphological features of the cloud image, the SAI interpolation and NSCT not only reconstruct the smooth component, but also maintain the texture and edge of the infrared nephogram.