Exploiting spatial sparsity for multi-wavelength imaging in optical interferometry

TL;DR

This paper introduces a novel multi-wavelength image reconstruction algorithm for optical interferometry that leverages spatial sparsity and spectral grouping to improve the quality of 3-D brightness distribution imaging of point-like astronomical sources.

Contribution

The paper presents a new regularization-based approach and a specialized optimization algorithm for joint 3-D image reconstruction from multi-wavelength interferometric data, focusing on point-like sources.

Findings

Enhanced spatial and spectral image quality using the proposed method

Effective regularization that promotes sparsity and spectral grouping

Development of a non-differentiable optimization algorithm for the problem

Abstract

Optical interferometers provide multiple wavelength measurements. In order to fully exploit the spectral and spatial resolution of these instruments, new algorithms for image reconstruction have to be developed. Early attempts to deal with multi-chromatic interferometric data have consisted in recovering a gray image of the object or independent monochromatic images in some spectral bandwidths. The main challenge is now to recover the full 3-D (spatio-spectral) brightness distribution of the astronomical target given all the available data. We describe a new approach to implement multi-wavelength image reconstruction in the case where the observed scene is a collection of point-like sources. We show the gain in image quality (both spatially and spectrally) achieved by globally taking into account all the data instead of dealing with independent spectral slices. This is achieved thanks to a regularization which favors spatial sparsity and spectral grouping of the sources. Since the objective function is not differentiable, we had to develop a specialized optimization algorithm which also accounts for non-negativity of the brightness distribution.