Computational frameworks for the fast Fourier transform
Computational frameworks for the fast Fourier transform
Deblurring Images: Matrices, Spectra, and Filtering (Fundamentals of Algorithms 3) (Fundamentals of Algorithms)
Iterative Algorithms Based on Decoupling of Deblurring and Denoising for Image Restoration
SIAM Journal on Scientific Computing
An Efficient Iterative Approach for Large-Scale Separable Nonlinear Inverse Problems
SIAM Journal on Scientific Computing
Practical image deblurring with synthetic boundary conditions, with gpus, and with multiple frames
Practical image deblurring with synthetic boundary conditions, with gpus, and with multiple frames
Extensions of the Justen---Ramlau blind deconvolution method
Advances in Computational Mathematics
| Hi-index | 7.29 |
Obtaining high resolution images of space objects from ground based telescopes involves using a combination of sophisticated hardware and computational post-processing techniques. An important, and often highly effective, computational post processing tool is multiframe blind deconvolution (MFBD). Mathematically, MFBD is modeled as a nonlinear inverse problem that can be solved using a flexible, variable projection optimization approach. In this paper we consider MFBD problems that are parameterized by a large number of variables. The formulas required for efficient implementation are carefully derived using the spectral decomposition and by exploiting properties of conjugate symmetric vectors. In addition, a new approach is proposed to provide a mathematical decoupling of the optimization problem, leading to a block structure of the Jacobian matrix. An application in astronomical imaging is considered, and numerical experiments illustrate the effectiveness of our approach.