We present a novel method of Bayesian image super-resolution in which marginalization is carried out over latent parameters such as geometric and photometric registration and the image point-spread function. Related Bayesian super-resolution approaches marginalize over the high-resolution image, necessitating the use of an unfavourable image prior, whereas our method allows for more realistic image prior distributions, and reduces the dimension of the integral considerably, removing the main computational bottleneck of algorithms such as Tipping and Bishop's Bayesian image super-resolution. We show results on real and synthetic datasets to illustrate the efficacy of our method.

This paper develops a multi-frame image super-resolution approach from a Bayesian view-point by marginalizing over the unknown registration parameters relating the set of input low-resolution views. In Tipping and Bishop’s Bayesian image super-resolution approach [16], the marginalization was over the super- resolution image, necessitating the use of an unfavorable image prior. By inte- grating over the registration parameters rather than the high-resolution image, our method allows for more realistic prior distributions, and also reduces the dimen- sion of the integral considerably, removing the main computational bottleneck of the other algorithm. In addition to the motion model used by Tipping and Bishop, illumination components are introduced into the generative model, allowing us to handle changes in lighting as well as motion. We show results on real and synthetic datasets to illustrate the efficacy of this approach.

The random sample consensus (RANSAC) algorithm, along with its many cousins such as MSAC and MLESAC, has become a standard choice for robust estimation in many computer vision problems. Recently, a raft of modifications to the basic RANSAC algorithm have been proposed aimed at improving its efficiency. Many of these optimizations work by reducing the number of hypotheses that need to be evaluated. This paper proposes a complementary strategy that aims to reduce the average amount of time spent computing the consensus score for each hypothesis. A simple statistical test is proposed that permits the scoring process be terminated early, potentially yielding large computational savings. The proposed test is simple to implement, imposes negligible computational overhead, and is effective for any given size of data set. The approach is evaluated by estimation of the fundamental matrix for a large number of image pairs and is shown to offer a significant reduction in computational cost compared to recently proposed RANSAC modifications. 1

The objective of the work presented is the super-resolution restoration of a set of images, and we investigate the use of learnt image models within a generative Bayesian framework. It is demonstrated that restoration of far higher quality than that determined by classical maximum likelihood estimation can be achieved by either constraining the solution to lie on a restricted sub-space, or by using the sub-space to define a spatially varying prior. This sub-space can be learnt from image examples. The methods are applied to both real and synthetic images of text and faces, and results are compared to R.R. Schultz and R.L. Stevenson's (1996) MAP estimator. We consider in particular images of scenes for which the point-to-point mapping is a plane projective transformation which has 8 degrees of freedom. In the real image examples, registration is obtained from the images using automatic methods.

The objective of this work is the super-resolution enhancement of image sequences. We consider in particular images of scenes for which the point-to-point image transformation is a plane projective transformation. We first describe the imaging model, and a maximum likelihood (ML) estimator of the super-resolution image. We demonstrate the extreme noise sensitivity of the unconstrained ML estimator. We show that the Irani and Peleg (1991, 1993) super-resolution algorithm does not suffer from this sensitivity, and explain that this stability is due to the error back-projection method which effectively constrains the solution. We then propose two estimators suitable for the enhancement of text images: a maximum a posteriori (MAP) estimator based on a Huber prior and an estimator regularized using the total variation norm. We demonstrate the improved noise robustness of these approaches over the Irani and Peleg estimator. We also show the effects of a poorly estimated point spread function (PSF) on the super-resolution result and explain conditions necessary for this parameter to be included in the optimization. Results are evaluated on both real and synthetic sequences of text images. In the case of the real images, the projective transformations relating the images are estimated automatically from the image data, so that the entire algorithm is automatic.

We describe mosaicing for a sequence of images acquired by a camera rotating about its centre. The novel contributions are in two areas. First, in the automation and estimation of image registration: images