On the basis of the degradation process of a blurred image, a convolution fuzzy model and the fuzzy image generation mechanism, a zero-norm regularization kernel estimation method is proposed to overcome the problem that 0 norm is difficult to solve in the remote sensing image reconstruction. By taking a fuzzy nuclear sparse for prior knowledge and corresponding gradient norms for regular items, the method avoids the impact of small edges of the image on blurred kernel and accurately estimates the blur kernel by the blurring image. Furthermore, the super Laplace distribution is used to approximate the heavy-tailed distribution of image gradient, and the norm regularization is taken to deconvolute the blurred image to recover the original image. As compared with the traditional methods, the proposed method estimates the obscure kernel of the image correctly, restrains the ringing phenomena well and improves the quality of remoter sensing image. The experiments for the same blade shows that Modulation Transfer Function(MTF) curve from proposed method is better than those from the blurred images and other reconstructed images.