Linear inversion of ban limit reflection seismograms
SIAM Journal on Scientific and Statistical Computing
Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Edge Direction Preserving Image Zooming: A Mathematical and Numerical Analysis
SIAM Journal on Numerical Analysis
Fundamental Limits of Reconstruction-Based Superresolution Algorithms under Local Translation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion-Free Super-Resolution
IEEE Transactions on Image Processing
A computationally efficient superresolution image reconstruction algorithm
IEEE Transactions on Image Processing
Imaging below the diffraction limit: a statistical analysis
IEEE Transactions on Image Processing
Super-resolution using sub-band constrained total variation
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
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In this paper we study the possibility of removing aliasing in a scene from a single observation by designing an alias-free upsampling scheme. We generate the unknown high frequency components of the given partially aliased (low resolution) image by minimizing the total variation of the interpolant subject to the constraint that part of unaliased spectral components in the low resolution observation are known precisely and under the assumption of sparsity in the data. This provides a mathematical basis for exact reproduction of high frequency components with probability approaching one, from their aliased observation. The primary application of the given approach would be in super-resolution imaging.