In applications that demand highly detailed images, it is often not feasible or sometimes possible to acquire images of such high resolution by just using hardware (high precision optics and charge coupled devices). Instead, image processing approaches can be used to construct a high resolution image from multiple, degraded, low resolution images. It is assumed that the low resolution images have been subsampled (thus introducing aliasing) and displaced by sub-pixel shifts with respect to a reference frame. Therefore, the problem can be divided into three sub-problems: registration (estimating the shifts), restoration, and interpolation. None of the methods which appeared in the literature solve the registration and restoration sub-problems simultaneously. This is sub-optimal, since the registration and restoration steps are inter-dependent. Based on previous restoration and identification work using the Expectation-Maximization algorithm, the proposed approach estimates the sub-pixel shifts and conditional mean (restored images) simultaneously. In addition, the registration and restoration sub-problems are cast in a multi-channel framework to take advantage of the cross- channel information. Experimental results show the validity of this method.