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Removing Atmospheric Turbulence via Space-Invariant Deconvolution
Jan. 2013 (vol. 35 no. 1)
pp. 157-170
Xiang Zhu, Dept. of Electr. Eng., Univ. of California, Santa Cruz, Santa Cruz, CA, USA
P. Milanfar, Dept. of Electr. Eng., Univ. of California, Santa Cruz, Santa Cruz, CA, USA
To correct geometric distortion and reduce space and time-varying blur, a new approach is proposed in this paper 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 B-spline-based nonrigid registration. Next, a temporal regression process is carried out to produce an image from the registered frames, which can be viewed as being convolved with a space invariant near-diffraction-limited blur. Finally, a blind deconvolution algorithm is implemented to deblur the fused image, generating a final 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.
Index Terms:
splines (mathematics),deconvolution,geometry,image registration,image restoration,image sequences,regression analysis,fused image,atmospheric turbulence,space-invariant deconvolution,geometric distortion,time-varying blur,image sequence,time-varying deblurring problem,geometric deformation,B-spline-based nonrigid registration,temporal regression process,space invariant near-diffraction-limited blur,Image restoration,Vectors,Imaging,Deconvolution,Noise,Kernel,Estimation,sharpness metric,Image restoration,atmospheric turbulence,nonrigid image registration,point spread function
Citation:
Xiang Zhu, P. Milanfar, "Removing Atmospheric Turbulence via Space-Invariant Deconvolution," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 1, pp. 157-170, Jan. 2013, doi:10.1109/TPAMI.2012.82
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