Comparison of maximum-likelihood image and wavefront reconstruction using conventional image, phase diversity, and lenslet diversity data

Abstract

An image reconstruction approach is developed that makes joint use of image sequences produced by a conventional imaging channel and a Shack-Hartmann (lenslet) channel. Iterative maximization techniques are used to determine the reconstructed object that is most consistent with both the conventional and Shack-Hartmann raw pixel-level data. The algorithm is analogous to phase diversity, but with the wavefront diversity provided by a lenslet array rather than a simple defocus. The log-likelihood cost function is matched to the Poisson statistics of the signal and Gaussian statistics of the detector noise. Addition of a cost term that encourages the estimated object to agree with a priori knowledge of an ensemble averaged power spectrum regularizes the reconstruction. Techniques for modeling FPA sampling are developed that are convenient for performing both the forward simulation and the gradient calculations needed for the iterative maximization. The model is computationally efficient and accurately addresses all aspects of the Shack-Hartmann sensor, including subaperture cross-talk, FPA aliasing, and geometries in which the number of pixels across a subaperture is not an integer. The performance of this approach is compared with multi-frame blind deconvolution and phase diversity using simulations of image sequences produced by the visible band GEMINI sensor on the AMOS 1.6 meter telescope. It is demonstrated that wavefront information provided by the second channel improves image reconstruction by avoiding the wavefront ambiguities associated with multiframe blind deconvolution and to a lesser degree, phase diversity.