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Restoration of Atmospheric Turbulence-distorted images via RPCA and Quasi-conformal Maps

Project Description:

We address the problem of restoring a high-quality image from an observed image sequence strongly distorted by atmospheric turbulence. A novel algorithm is proposed in this paper to reduce geometric distortion as well as space and time-varying blur due to turbulence. By considering an optimization problem, our algorithm first obtain a sharp reference image and a sub-sampled image sequence containing sharp and mildly distorted image frames with respect to the reference image. The sub-sampled image sequence is then stabilized by applying the Robust Principal Component Analysis (RPCA) on the deformation fields between image frames and warping the image frames by a quasiconformal map associated to the low-rank part of the deformation matrix. After image frames are registered to the reference image, the low-rank part of them are deblurred via a blind deconvolution, and the deblurred frmaes are then fused with the enhanced sparse part. Experiments have been carried out on both synthetic and real turbulence-distorted video. Results demonstrate our method is effecitve  in alleviating distortions and blur, restoring image details and enhancing visual quality.


Publication:

  • C.P. Lau, Y.H. Lai, L.M. Lui, Restoration of atmospheric turbulence-distorted images via RPCA and Quasiconformal maps, to be submitted (2017)

 

 

Orignal distorted video:                                            Stablized video using our proposed method:

 

Final restored high-quality image by our proposed method

Orignal distorted video:                                            Stablized video using our proposed method:

 

Final restored high-quality image by our proposed method

Orignal distorted video:                                            Stablized video using our proposed method:

Final restored high-quality image by our proposed method