Abstract
SummaryWe present an iterative approach to solve separable nonlinear least squares problems arising in the estimation of wavelength‐dependent point spread function parameters for hyperspectral imaging. A variable projection Gauss–Newton method is used to solve the nonlinear least squares problem. An analysis shows that the Jacobian can be potentially very ill conditioned. To deal with this ill conditioning, we use a combination of subset selection and other regularization techniques. Experimental results related to hyperspectral point spread function parameter identification and star spectrum reconstruction illustrate the effectiveness of the resulting numerical scheme. Copyright © 2015 John Wiley & Sons, Ltd.
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