ENAS-RIF algorithm for image restoration

Imaging of objects is inevitably encountered by space-based, ground-based working in the atmospheric turbulence environment, such as those used in astronomy, remote sensing and so on. The observed images are seriously blurred. The restoration is required for reconstruction turbulence degraded images. In order to enhance the performance of image restoration, a novel enhanced nonnegativity and support constrants recursive inverse filtering(ENAS-RIF) algorithm was presented, which was based on the reliable support region and enhanced cost function. Firstly, the Curvelet denoising algorithm was used to weaken image noise. Secondly, the reliable object support region estimation was used to accelerate the algorithm convergence. Then, the average gray was set as the gray of image background pixel. Finally, an object construction limit and the logarithm function were add to enhance algorithm stability. The experimental results prove that the convergence speed of the novel ENAS-RIF algorithm is faster than that of NAS-RIF algorithm and it is better in image restoration.