Related Experiment Video
Updated: Jun 4, 2026

Determining 3D Flow Fields via Multi-camera Light Field Imaging
Published on: March 6, 2013
Deconvolution from wave front sensing using the frozen flow hypothesis
Stuart M Jefferies1, Michael Hart
1Institute for Astronomy, University of Hawaii, 34 Ohia Ku Street, Pukalani, HI 96768, USA. stuartj@ifa.hawaii.edu
Abstract:
Deconvolution from wave front sensing (DWFS) is an image-reconstruction technique for compensating the image degradation due to atmospheric turbulence. DWFS requires the simultaneous recording of high cadence short-exposure images and wave-front sensor (WFS) data. A deconvolution algorithm is then used to estimate both the target object and the wave front phases from the images, subject to constraints imposed by the WFS data and a model of the optical system. Here we show that by capturing the inherent temporal correlations present in the consecutive wave fronts, using the frozen flow hypothesis (FFH) during the modeling, high-quality object estimates may be recovered in much worse conditions than when the correlations are ignored.
More Related Videos
Related Concept Videos
Uniform Depth Channel Flow: Problem Solving
Uniform Depth Channel Flow
Deconvolution
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
Fast Decoupled and DC Powerflow
Turbulent Flow: Problem Solving
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures enhance...
Rapidly Varying Flow

