Abstract: The deblurred images obtained by traditional frequency-wavelet domain based image restoration algorithms always result in prominent boundary ringing and smoothing artifacts. And the more effective space domain based alternating restoration algorithms usually work slowly. To overcome these problems, an algorithm named TwIST-TV which combines the two-step iterative shrinkage/thresholding (TwIST) and total variation (TV) regularization were proposed. This method first introduced the TV regularization constraint on the objective function, and then applied the TV-denoising method to regularize the midrestored image in each iteration before whose wavelet coefficients were processed by the TwIST method, and eventually obtained the deblurred image. Experimental results show that, in contrast to the frequencywavelet domain based image restoration algorithms, TwIST-TV can effectively suppress the boundary ringing and smoothing artifacts. The restored images can achieve 1-7 dB higher values of the signal-tonoise ratio (SNR), the peak signal-to-noise ratio (PSNR) and 0.05 higher value of the mean structural similarity (MSSIM) index. Proposed method has more than 6 times the speed advantage comparied with the methods which need alternating optimization in the space domain while maintain the accuracy of the solution.