Removing Atmospheric Turbulence Effects Via Geometric Distortion and Blur Representation
Hua Xia, Chao Pan, Yu Shi, Jianguo Liu, Hanyu Hong
Abstract
Removing the geometric distortion and space-time-varying blur caused by atmospheric turbulence from a given image sequence remains a challenge. Since geometric distortion and blur are two different kinds of distortions and interact with each other in the process of image restoration, it is difficult to extract the features that are useful to the restoration process when the images experience multiple distortions. In this article, we propose a new scheme based on geometric distortion and blur representation. The blur invariants and maximum gradient are used to represent the geometric distortion and sharpness of an image frame, respectively. The proposed scheme consists of three parts. First, two fast frame selection algorithms based on independent evaluations of the sharpness and geometric distortion are proposed to subsample a sharp subsequence and obtain a reference image. Next, to suppress the geometric distortion, a moment-blur-invariant-based method is presented to estimate the deformation vector between two degraded frames, and the selected sharp frames are registered to the reference image. Finally, a blind deconvolution method is applied to deblur the fused image, generating a final restoration result. Various experimental results show that the proposed method can effectively alleviate distortion and blur, as well as significantly improve the visual quality of real atmospheric turbulence-degraded images.