An improved regularization blind restoration method based on L0 sparse prior was proposed to overcome the image blue from camera shake. A new optimization mode on the basis of inherent property which the gradient distribution of the blurred image is denser than that of the clear image and the sparse of the dark channel is relatively smaller. Aiming at the highly non-convex of L0 norm and nonlinear minimization problem in the dark channel sparse optimization process, an approximate linear map matrix based on look-up tables was proposed, and the linearized L0 minimization problem was solved by half-quadratic splitting methods. Finally, the fast Fourier transform was used to do iterative operation alternately for the fuzzy kernel and the clear image in frequency domain to obtain the restored image. Through experiments on several different types of blurred images, the results show that average gray level gradient is up to 11.411, the image entropy is up to 7.304, and it only takes 807s to process 365×285 images. The improved regularization algorithm effectively suppresses the ringing effect near the edge of the image, retains the integrity of clear details, improves the speed of operation significantly. The algorithm is suitable for all kinds of image restoration.