From Event: SPIE Optical Engineering + Applications, 2017
It is well known that passive image correction of turbulence distortions often involves using geometry-dependent deconvolution algorithms. On the other hand, active imaging techniques using adaptive optic correction should use the distorted wavefront information for guidance. Our work shows that a hybrid hardware-software approach is possible to obtain accurate and highly detailed images through turbulent media. The processing algorithm also takes much fewer iteration steps in comparison with conventional image processing algorithms. In our proposed approach, a plenoptic sensor is used as a wavefront sensor to guide post-stage image correction on a high-definition zoomable camera. Conversely, we show that given the ground truth of the highly detailed image and the plenoptic imaging result, we can generate an accurate prediction of the blurred image on a traditional zoomable camera. Similarly, the ground truth combined with the blurred image from the zoomable camera would provide the wavefront conditions. In application, our hybrid approach can be used as an effective way to conduct object recognition in a turbulent environment where the target has been significantly distorted or is even unrecognizable.
Chensheng Wu, John Robertson Rzasa, Jonathan Ko, and Christopher C. Davis, "Hybrid wavefront sensing and image correction algorithm for imaging through turbulent media," Proc. SPIE 10408, Laser Communication and Propagation through the Atmosphere and Oceans VI, 104080Y (Presented at SPIE Optical Engineering + Applications: August 09, 2017; Published: 7 September 2017); https://doi.org/10.1117/12.2275735.
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