Title : 
Removing Atmospheric Turbulence via Space-Invariant Deconvolution
         
        
            Author : 
Xiang Zhu ; Milanfar, Peyman
         
        
            Author_Institution : 
Dept. of Electr. Eng., Univ. of California, Santa Cruz, Santa Cruz, CA, USA
         
        
        
        
        
        
        
        
            Abstract : 
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.
         
        
            Keywords : 
deconvolution; geometry; image registration; image restoration; image sequences; regression analysis; splines (mathematics); B-spline-based nonrigid registration; atmospheric turbulence; fused image; geometric deformation; geometric distortion; image sequence; space invariant near-diffraction-limited blur; space-invariant deconvolution; temporal regression process; time-varying blur; time-varying deblurring problem; Deconvolution; Estimation; Image restoration; Imaging; Kernel; Noise; Vectors; Image restoration; atmospheric turbulence; nonrigid image registration; point spread function; sharpness metric; Algorithms; Artifacts; Artificial Intelligence; Atmosphere; Image Enhancement; Image Interpretation, Computer-Assisted;
         
        
        
            Journal_Title : 
Pattern Analysis and Machine Intelligence, IEEE Transactions on
         
        
        
        
        
            DOI : 
10.1109/TPAMI.2012.82