The traditional regularization blind restoration methods employ the same norm of the fidelity term and the regularization term for both the image and the Point Spread Function (PSF), which decreases the quality of restored image as well as the accuracy of estimated PSF. Therefore, this paper proposes a multi-norm hybrid constrained regularization method for image blind restoration. First, the L1 norm and the Total Variation (TV) norm are respectively adopted as the fidelity term and the regularization term for the image to eliminate the stair-casing effects and preserve the edges. Then, the L2 norm and the H1 norm are respectively adopted as the fidelity term and regularization term for the PSF to reduce the difficulty of the PSF estimation. Finally, the split Bregman iteration method is used to address the proposed model. Experiments are conducted on both synthetic and real-life degradations, and the results indicate that the proposed method can effectively restore a variety of artificial blurs such as motion blur, out-of-focus blur etc and can estimate the corresponding PSF accurately. Comparing to some other recent blind restoration methods, the proposed method can drastically improve the image quality in term of subjective visual and the Improved Signal-to-noise Ratio (ISNR) is improved from 0.36 dB to 14.66 dB.