The aim of image restoration is to recover the original uncorrupted images from noisy, blurred ones. A linear image restoration algorithm based on a wavelet-domain local gaussian model is proposed in this paper. The wavelet-domain local gaussian model approximates the local probability distribution of the wavelet coefficients with a single gaussian function. Because the wavelet-domain local gaussian model adaptively characterizes the local statistic properties of real-world images, the algorithm presented in this paper specifies the prior distribution of the real-world image through the model and converts the restoration problem to an constrained optimization one which can be solved with the conjugate gradient method. Experimental results show that the algorithm can properly retrieve various kinds of edges, and the PNSR (peak signal to noise ratio) and subjective visual effect of the restored images are improved significantly.