Many atmospheric turbulence deblurring techniques estimate an inverse filter by making assumptions that constrain the mathematical spaces in which an unknown signal and convolving function must reside. Restoration of scene content after imaging through terrestrial imaging paths is an area of active experimentation and development for both real-time feature extraction and post-process data reduction. Static scenes present opportunities for algorithms that exploit the temporal diversity of the atmospheric path since motion of scene content at the image plane over multiple frames may be attributed to a randomly varying blur kernel. This allows for the estimation of inverse filters that can be used to deblur the image. However, when objects in the scene move relative to one another across multiple image frames it complicates an already computationally demanding process. Techniques to compensate for the motion of one or more features can be used, but if the image fidelity is insufficient to detect a moving feature in the first place or the number of features (e.g. fragmentation from an impact or explosion) is very large, motion compensation techniques may break down or become impractical. In this paper we explore using multiple, synchronized optical systems with sufficient spatial separation to provide the optical path turbulence diversity required by many deblurring algorithms. This reduces or eliminates many constraints on object motion when performing reconstructions. We present deblurred imagery examples from an experimental setup that leverages spatially diverse, optical path turbulence and compare the results with the traditional approach of utilizing single path, temporal diversity when performing image reconstructions. Our results demonstrate that: (1) useful deblurring is possible with a single “set” of images simultaneously collected through diverse optical paths, (2) a combination of temporal and spatial diversity of image collection can be a useful “hybrid” approach, and (3) opportunistic weighting of concurrent frames according to image quality can enhance the deblurring results.
Thomas A. Underwood, Joe Stufflebeam, David Soules, Mark Kircher, Mark Roberts, Joachim Lohn-Jaramillo, Geoff Knox, and Jason Shankle, "Exploring mitigation of image blur due to atmospheric turbulence by utilizing multiple sensors to achieve optical path diversity," Proc. SPIE 10650, Long-Range Imaging III, 1065005 (Presented at SPIE Defense + Security: April 17, 2018; Published: 11 May 2018); https://doi.org/10.1117/12.2309412.
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