Super-resolution based on interpolation and global sub pixel translation

In this paper we present a new class of reconstruction algorithms that are basically different from the traditional approaches. We deviate from the traditional technique which treats the pixels of the image as point samples. In this work, the pixels are treated as rectangular surface samples. It is in conformity with image formation process, in particular for CCD/CMOS sensors, which are a matrix of rectangular surfaces sensitive to the light. We show that results of better quality in terms of the measurements employed are obtained by formulating the reconstruction as a two-stage process: the restoration of image followed by the application of the point spread function (PSF) of the imaging sensor. By coupling the PSF with the reconstruction process, we satisfy a measure of accuracy that is based on the physical limitations of the sensor. Effective techniques for the restoration of image are derived to invert the effects of the PSF and estimate the original image. For the algorithm of restoration, we introduce a new method of interpolation implying a sequence of images, not necessarily a temporal sequence, shifted compared to an image of reference.