Restoration of Atmospheric Turbulence-distorted Images via RPCA and Quasiconformal Maps
This addresses the problem of image restoration for applications like surveillance or astronomy, but it is incremental as it builds on existing methods like RPCA and quasiconformal maps.
The paper tackles the problem of restoring high-quality images from sequences distorted by atmospheric turbulence, proposing a novel algorithm that reduces geometric distortion and space-and-time-varying blur, with results showing effectiveness in alleviating distortions, restoring details, and enhancing visual quality in synthetic and real videos.
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 considering a suitable energy functional, our algorithm first obtains a sharp reference image and a subsampled image sequence containing sharp and mildly distorted image frames with respect to the reference image. The subsampled 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 with 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 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.