@inproceedings{
chen2023multilook,
title={Multilook compressive sensing in the presence of speckle noise},
author={Xi Chen and Zhewen Hou and Christopher Metzler and Arian Maleki and Shirin Jalali},
booktitle={NeurIPS 2023 Workshop on Deep Learning and Inverse Problems},
year={2023},
url={https://openreview.net/forum?id=G8wMnihF6E}
}
Multiplicative speckle noise is an inherent part of coherent imaging systems, such as synthetic aperture radar and digital holography. Speckle noise is mitigated by obtaining multiple measurement vectors with independent speckle noise, a technique commonly referred to as "multi-look", followed by appropriate averaging. However, in many applications, even with multi-look, the  achievable performance  is not satisfactory. Moreover, in this approach, every look (or every set of measurements) is required to be over-determined,which imposes additional constraints on spatial resolution. In this work, we develop a maximum likelihood based approach for recovering images from a set of compressive measurements contaminated by speckle noise. We propose an iterative multi-look compressive sensing recovery algorithm, DIP-$M^3$, that i) requires no training data, ii) is computationally efficient, and iii) generates high-quality reconstruction images from multi-look, where each look is underdetermined and corrupted by speckle noise.
Multilook compressive sensing in the presence of speckle noise
Xi Chen Zhewen Hou Christopher Metzler Arian Maleki Shirin Jalali