Restoration of Atmospherically Degraded Images using a Sparse Prior

The reconstruction of turbulence-affected images has been an active research topic in the field of astronomical imaging. Many approaches have been proposed in the literature. Recently, researchers have extended the methods to the recovery of long-path territorial natural scene surveillance, which is affected even more by air turbulence. Some approaches from astronomical imaging also work well in the latter problem. However, although these methods have involved statistics, such as a statistical model of atmospheric turbulence or the probability distribution of photons forming an image, they have not taken account of the statistical properties of natural scenes observed in long-path horizontal imagery. Recent research that a real world image generally has a sparse distribution of by others has made use of the fact that its derivatives. In this paper, we investigate algorithms with such a constraint imposed during the restoration of turbulence-affected images. This paper proposes an iterative, blind deconvolution algorithm that follows a registration and averaging method to remove anisoplanatic warping in a. time sequence of degraded images. The use of a sparse prior helps to reduce noise, produce, sharper edges and remove unwanted artifacts in the eestimated image for the reason that it pushes only a small number of pixels to have non-zero (or large) derivatives. We test the new algorithm with simulated and natural data and experiments show that it performs well.