A common and important problem that arises in visual communications is the need to create an enhanced-resolution video image sequence from a lower resolution input video stream. This can be accomplished by exploiting the spatial correlations that exist between successive video frames using super-resolution (SR) reconstruction. SR refers to the task of increasing the spatial resolution through multiple frame processing.

Multi-frame resolution enhancement methods are of increasing interest in digital image processing and there has been a substantial amount of research in developing algorithms that combine a set of low-quality images to produce a set of higher quality images. Either explicitly or implicitly, such algorithms must perform the common task of registering and fusing the low-quality image data. While many such processes have been proposed, very little work has addressed their limits.

In this context, an algorithm designed to operate in the spatial domain is used in a controlled test to compute a higher-resolution image by mapping a model of the image formation process using local sub-pixel shifts among the lower resolution and compressed images of the same scene. These shifts are determined by way of a rigorous least-squares area-based image-matching scheme that does not require control points.

Statistical results show that the performance of the algorithm does degrade, as would be expected, depending on (a) the amount of noise present in the low-resolution images, (b) the number of low-resolution input images and (c) the magnification factor required to meet resolution requirements.