Super-resolution from unregistered aliased images with unknown scalings and shifts

Abstract

We consider the problem of super-resolution from unregistered aliased images with unknown spatial scaling factors and shifts. Due to the limitation of pixel size in the image sensor, the sampling rate for each image is lower than the Nyquist rate of the scene. Thus, we have aliasing in captured images, which makes it hard to register the low-resolution images and then generate a high-resolution image. To work out this problem, we formulate it as a multichannel sampling and reconstruction problem with unknown parameters, spatial scaling factors and shifts. We can estimate the unknown parameters and then reconstruct the high-resolution image by solving a nonlinear least square problem using the variable projection method. Experiments with synthesized 1-D signals and 2-D images show the effectiveness of the proposed algorithm.

Fast and Accurate Multi-Frame Super-Resolution of Satellite Images

“…The low resolution images are usually first registered. However, the registration can also be refined during the minimization process (Tom, Katsaggelos, 1995, Peng et al, 2012. The cost function integrates a data term and a prior term.…”

Super-resolution: a comprehensive survey

“…In addition to the above mentioned methods in the frequency domain, some other SR algorithms of this domain have borrowed the methods that have been usually used in the spatial domain; among them are: [119], [211], [321], [370], [589] which have used a Maximum Likelihood (ML) method (Section 5.1.5), [144], [178], [201] which have used a regularized ML method, [197], [221], [267], [491], [511], [567] which have used a MAP method (Section 5.1.6), and [141], [175] which have implemented a Projection Onto Convex Set (POCS) method (Section 5.1.4). These will all be explained in the next section.…”