Choosing Regularization Parameters in Iterative Methods for Ill-Posed Problems
SIAM Journal on Matrix Analysis and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Gradient Based Weighted Averaging Method for Estimation of Fingerprint Orientation Fields
DICTA '05 Proceedings of the Digital Image Computing on Techniques and Applications
Super-resolution reconstruction in a computational compound-eye imaging system
Multidimensional Systems and Signal Processing
Deterministic edge-preserving regularization in computed imaging
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Nonlinear image recovery with half-quadratic regularization
IEEE Transactions on Image Processing
Source localization using a sparse representation framework to achieve superresolution
Multidimensional Systems and Signal Processing
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This paper discusses the problem of superresolution reconstruction. To preserve edges accurately and efficiently in the reconstruction, we propose a nonlinear gradient-based regularization that uses the gradient vector field of a preliminary high resolution image to configure a regularization matrix and compute the regularization parameters. Compared with other existing methods, it not only enhances the spatial resolution of the resulting images, but can also preserve edges and smooth noise to a greater extent. The advantages are shown in simulations and experiments with synthetic and real images.