Abstract-The accurate registration of multiview images is of central importance in many advanced image processing applications. Image super-resolution, for example, is a typical application where the quality of the super-resolved image is degrading as registration errors increase. Popular registration methods are often based on features extracted from the acquired images. The accuracy of the registration is in this case directly related to the number of extracted features and to the precision at which the features are located: images are best registered when many features are found with a good precision. However, in low-resolution images, only a few features can be extracted and often with a poor precision. By taking a sampling perspective, we propose in this paper new methods for extracting features in low-resolution images in order to develop efficient registration techniques. We consider, in particular, the sampling theory of signals with finite rate of innovation [10] and show that some features of interest for registration can be retrieved perfectly in this framework, thus allowing an exact registration. We also demonstrate through simulations that the sampling model which enables the use of finite rate of innovation principles is well suited for modeling the acquisition of images by a camera. Simulations of image registration and image superresolution of artificially sampled images are first presented, analyzed and compared to traditional techniques. We finally present favorable experimental results of super-resolution of real images acquired by a digital camera available on the market.

Recently, new sampling schemes were presented for signals with finite rate of innovation (FRI) using sampling kernels reproducing polynomials or exponentials [1] [2].In this paper, we extend those sampling schemes to a distributed acquisition architecture in which numerous and randomly located sensors are pointing to the same area of interest. We emphasize the importance played by moments and show how to acquire efficiently FRI signals with a set of sensors. More importantly, we also show that those sampling schemes can be used for accurate registration of affine transformed and low-resolution images. Based on this, a new super-resolution algorithm was developed and showed good preliminary results.

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The problem of image super-resolution from a set of low resolution multiview images has recently received much attention and can be decomposed, at least conceptually, into two consecutive steps as: registration and restoration. The ability to accurately register the input images is key to the success and the quality of image superresolution algorithms. Using recent results from the sampling theory for signals with Finite Rate of Innovation (FRI), we propose in this paper a new technique for subpixel extraction from low resolution images of local features like step edges and corners for image registration. By exploiting the knowledge of the sampling kernel, we are able to locate exactly the step edges on synthetic images. We also present results of full frame super-resolution of real low resolution images using our registration technique. We obtain super-resolved images with a much improved visual quality compared to using a standard local feature detection approach like a subpixel Harris corner detector.

Continuous-domain visual signals are usually captured as discrete (digital) images. This operation is not invertible in general, in the sense that the continuous-domain signal cannot be exactly reconstructed based on the discrete image, unless it satisfies certain constraints (\emph{e.g.}, bandlimitedness). In this paper, we study the problem of recovering shape images with smooth boundaries from a set of samples. Thus, the reconstructed image is constrained to regenerate the same samples (consistency), as well as forming a shape (bilevel) image. We initially formulate the reconstruction technique by minimizing the shape perimeter over the set of consistent binary shapes. Next, we relax the non-convex shape constraint to transform the problem into minimizing the total variation over consistent non-negative-valued images. We also introduce a requirement (called reducibility) that guarantees equivalence between the two problems. We illustrate that the reducibility property effectively sets a requirement on the minimum sampling density. One can draw analogy between the reducibility property and the so-called restricted isometry property (RIP) in compressed sensing which establishes the equivalence of the $\ell_0$ minimization with the relaxed $\ell_1$ minimization. We also evaluate the performance of the relaxed alternative in various numerical experiments.Comment: 13 pages, 14 figure