IEEE Transactions on Image Processing
Bootstrap resampling for image registration uncertainty estimation without ground truth
IEEE Transactions on Image Processing
Performance of reconstruction-based super-resolution with regularization
Journal of Visual Communication and Image Representation
Efficient Fourier-wavelet super-resolution
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Coordinate-descent super-resolution and registration for parametric global motion models
Journal of Visual Communication and Image Representation
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Accurate registration of images is the most important and challenging aspect of multiframe image restoration problems such as super-resolution. The accuracy of super-resolution algorithms is quite often limited by the ability to register a set of low-resolution images. The main challenge in registering such images is the presence of aliasing. In this paper, we analyse the problem of jointly registering a set of aliased images and its relationship to super-resolution. We describe a statistically optimal approach to multiframe registration which exploits the concept of variable projections to achieve very efficient algorithms. Finally, we demonstrate how the proposed algorithm offers accurate estimation under various conditions when standard approaches fail to provide sufficient accuracy for super-resolution.