Removing Atmospheric Turbulence for Long-Distance Imaging

Wednesday, September 25, 2013 - 11:30am - 12:20pm
Lind 305
Peyman Milanfar (University of California)
A new approach is proposed, capable of restoring a single high-quality image from a given image sequence distorted by atmospheric turbulence. This approach reduces the space and time-varying deblurring problem to a shift invariant one. It first registers each frame to suppress geometric deformation through non-rigid registration. Next, a temporal regression (fusion) process is carried out to produce an image from the registered frames, which can be viewed as being convolved with a space invariant diffraction limited blur. Finally, a blind deconvolution algorithm is implemented to deblur the fused image, generating a high quality output. Experiments using real data illustrate that this approach can effectively alleviate blur and distortions, recover details of the scene, and significantly improve visual quality.
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