A circulate iterative blind deconvolution (CIBD) algorithm to restore turbulence-degraded images in the frequency domain is described, which focus on enhancement of accuracy and rapidity. The accuracy is enhanced by beginning with random frame and gradually increasing new frame to circulate iteration to get satisfactory restoration. Some measures are implemented to improve convergence rate, such as estimated point spread functions (PSF) by correlation matrix, adaptive steplength updating rule, condition of iterative termination and scaled projection gradient strategy. The experimental results of simulation show that the algorithm is efficient to reconstruct the degraded images from remote ground-based observations under different atmospheric turbulence intensities, and its performance is better than traditional multiframe blind deconvolutions (MBDs).