Multi-frame super-resolution restoration refers to techniques for
still-image and video restoration which utilize multiple observed
images of an underlying scene to achieve the restoration of
super-resolved imagery. An observation model which relates the
measured data to the unknowns to be estimated is formulated to account
for the registration of the multiple observations to a fixed reference
frame as well as for spatial and temporal degradations resulting from
characteristics of the optical system, sensor system and scene motion.
Linear observation models, in which the observation process is
described by a linear transformation, have been widely adopted. In
this paper we consider the application of the linear observation model
to multi-frame super-resolution restoration under conditions of
non-affine image registration and spatially varying PSF. Reviewing earlier results, we show how these conditions relate to the technique of image warping from the computer graphics literature and how these ideas may be applied to multi-frame restoration. We illustrate the application of these methods to multi-frame super-resolution restoration using a Bayesian inference framework to solve the ill-posed restoration inverse problem.
Multi-frame super-resolution restoration algorithms commonly utilize a linear observation model relating the recorded images to the unknown restored image estimates. Working within this framework, we demonstrate a method for generalizing the observation model to incorporate spatially varying point spread functions and general motion fields. The method utilizes results from image resampling theory which is shown to have equivalences with the multi-frame image observation model used in super-resolution restoration. An algorithm for computing the coefficients of the spatially varying observation filter is developed. Examples of the application of the proposed method are presented.
The performance of block-matching sub-pixel motion estimation algorithms under the adverse conditions of image undersampling and additive noise is studied empirically. This study is motivated by the requirement for reliable sub-pixel accuracy motion estimates for motion compensated observation models used in multi-frame super-resolution image reconstruction. Idealized test functions which include translational scene motion are defined. These functions are sub-sampled and corrupted with additive noise and used as source data for various block-matching sub-pixel motion estimation techniques. Motion estimates computed from this data are compared with the a-priori known motion which enables an assessment of the performance of the motion estimators considered.
NonGaussian Markov image models are effective in the preservation of edge detail in Bayesian formulations of restoration and reconstruction problems. Included in these models are coefficients quantifying the statistical links among pixels in local cliques, which are typically assumed to have an inverse dependence on distance among the corresponding neighboring pixels. Estimation of these coefficients is a nontrivial task for Non Gaussian models. We present rules for coefficient estimation for edge- preserving models which are particularly effective for edge preservation and noise suppression, using a predictive technique analogous to estimation of the weights of optimal weighted median filters.
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