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Video stabilization of atmospheric turbulence distortion
Yifei Lou
UCSD
Abstract:
We present a method to enhance the quality of a video sequence captured through a turbulent atmospheric medium. Enhancement is framed as the inference of the radiance of the distant scene, represented as a latent image," that is assumed to be constant throughout the video. Temporal distortion is thus zero-mean and temporal averaging produces a blurred version of the scene's radiance, that is processed via a Sobolev gradient flow to yield the latent image in a way that is reminiscent of the \lucky region" method. Without enforcing prior knowledge, we can stabilize the video sequence while preserving ne details. We also present the well-posedness theory for the stabilizing PDE and a linear stability analysis of the numerical scheme. This is a joint work with Sung Ha Kang, Stefano Soatto and Andrea Bertozzi.
Tuesday, May 8, 2012
11:00AM AP&M 2402
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Randolph E. Bank
Philip E. Gill
Michael Holst
Administrative Contact:
[ Click to Send Email ]
Yifei Lou
UCSD
Abstract:
We present a method to enhance the quality of a video sequence captured through a turbulent atmospheric medium. Enhancement is framed as the inference of the radiance of the distant scene, represented as a latent image," that is assumed to be constant throughout the video. Temporal distortion is thus zero-mean and temporal averaging produces a blurred version of the scene's radiance, that is processed via a Sobolev gradient flow to yield the latent image in a way that is reminiscent of the \lucky region" method. Without enforcing prior knowledge, we can stabilize the video sequence while preserving ne details. We also present the well-posedness theory for the stabilizing PDE and a linear stability analysis of the numerical scheme. This is a joint work with Sung Ha Kang, Stefano Soatto and Andrea Bertozzi.
Tuesday, May 8, 2012
11:00AM AP&M 2402