Restoring atmospheric-turbulence-degraded images

Image data experiences geometric distortions and spatial-temporal varying blur due to the strong effects of random spatial and temporal variations in the optical refractive index of the communication path. Simultaneously removing these effects from an image is a challenging task. An efficient approach is proposed in this paper to address this problem. The approach consists of four steps. First, a frame selection strategy is employed by proposing an unsupervised k-means clustering technique. Second, a B-spline-based nonrigid image registration is carried out to suppress geometric distortions. Third, a spatiotemporal kernel regression is proposed by introducing the local sharp patch concept to fuse the registered frame sequences into an image. Finally, a blind deconvolution technique is employed to deblur the fused image. Experiments are carried out with synthetic and real-world turbulence-degraded data by implementing the proposed method and two recently reported methods. The proposed method demonstrates significant improvement over the two reported methods in terms of alleviating blur and distortions, as well as improving visual quality.