Restoration of atmospheric turbulence-distorted images via RPCA and quasiconformal maps

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 strong turbulence. By measuring image sharpness and a quasiconformal measure on the deformation fields, our algorithm first obtains a sharp reference image and an image subsequence containing sharp and mildly distorted image frames with respect to the reference image. The image subsequence is then stabilized by applying robust principal component analysis on the deformation fields between image frames and warping the image frames by a quasiconformal map associated with the low-rank part of the deformation matrix. After image frames are registered to the reference image, the low-rank parts of them are deblurred via a blind deconvolution, and the deblurred frames are then fused with the enhanced sparse part. Experiments have been carried out on both synthetic and real turbulence-distorted video. Results demonstrate that our method is effective in alleviating distortions and blur, restoring image details and enhancing visual quality. © 2019 IOP Publishing Ltd