Variational Models for Joint Subsampling and Reconstruction of Turbulence-degraded ImagesMS49

Turbulence-degraded image frames are distorted by both turbulent deformations and space-time-varying blurs. To suppress these effects, we propose a multi-frame reconstruction scheme to recover a latent image from the observed image sequence. Commonly used approaches are based on registering each frame to a reference image, by which geometric turbulent deformations can be estimated and a sharp image can be restored. A major challenge is that a fine reference image is usually unavailable, as every turbulence-degraded frames are distorted. A high-quality reference image is crucial for the accurate extraction of geometric deformations and fusion of frames. Besides, it is unlikely that all frames from the image sequence are useful, and thus frame selection is necessary. In this work, we propose a variational model for joint subsampling of frames and extraction of a reference image. A fine reference image extracted from a suitable choice of subsample are simultaneously obtained by iteratively reducing an energy functional. The energy consists of a fidelity term measuring the discrepancy between the reference image and the subsampled frames, as well as a quality measure for each frame. Different choices of fidelity and quality terms are explored. By carefully selecting suitable frames and extracting the reference image, the quality of the reconstructed image can be significantly improved. Extensive experiments have been carried out, which demonstrate the efficacy of our proposed model.

This presentation is part of Minisymposium “MS49 - Image Restoration, Enhancement and Related Algorithms (4 parts)
organized by: Weihong Guo (Case Western Reserve University) , Ke Chen (University of Liverpool) , Xue-Cheng Tai (Hong Kong Baptist University) , Guohui Song (Clarkson University) .

Ronald Lui (Chinese University of Hong Kong)
Chun Pong Lau (The Chinese University of Hong Kong)