Fast Deformable Registration on the GPU: A CUDA Implementation of Demons
ICCSA '08 Proceedings of the 2008 International Conference on Computational Sciences and Its Applications
Optimization of mutual information for multiresolution image registration
IEEE Transactions on Image Processing
Resolution enhancement of monochrome and color video using motion compensation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
Multiframe demosaicing and super-resolution of color images
IEEE Transactions on Image Processing
Super-Resolution Based on Fast Registration and Maximum a Posteriori Reconstruction
IEEE Transactions on Image Processing
An Expanded Theoretical Treatment of Iteration-Dependent Majorize-Minimize Algorithms
IEEE Transactions on Image Processing
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Motion artifacts in image reconstruction problems can be reduced by performing image motion estimation and image reconstruction jointly using a penalized-likelihood cost function. However, updating the motion parameters by conventional gradient-based iterations can be computationally demanding due to the system model required in inverse problems. This paper describes an optimization transfer approach that leads to minimization steps for the motion parameters that have comparable complexity to those needed in image registration problems. This approach can simplify the implementation of motion-compensated image reconstruction (MCIR) methods when the motion parameters are estimated jointly with the reconstructed image.