Space optical images are inevitably degraded by atmospheric turbulence, error of the optical system and motion. In order to get the true image, a novel nonnegativity and support constants recursive inverse filtering (NAS-RIF) algorithm is proposed to restore the degraded image. Firstly，the image noise is weaken by Contourlet denoising algorithm. Secondly, the reliable object support region estimation is used to accelerate the algorithm convergence. We introduce the optimal threshold segmentation technology to improve the object support region. Finally, an object construction limit and the logarithm function are added to enhance algorithm stability. Experimental results demonstrate that, the proposed algorithm can increase the PSNR, and improve the quality of the restored images. The convergence speed of the proposed algorithm is faster than that of the original NAS-RIF algorithm.
Adaptive Optics together with subsequent post-processing techniques obviously improve the resolution of turbulencedegraded images in ground-based space objects detection and identification. The most common method for frame selection and stopping iteration in post-processing has always been subjective viewing of the images due to a lack of widely agreed-upon objective quality metric. Full reference metrics are not applicable for assessing the field data, no-reference metrics tend to perform poor sensitivity for Adaptive Optics images. In the present work, based on the Laplacian of Gaussian (LOG) local contrast feature, a nonlinear normalization is applied to transform the input image into a normalized LOG domain; a quantitative index is then extracted in this domain to assess the perceptual image quality. Experiments show this no-reference quality index is highly consistent with the subjective evaluation of input images for different blur degree and different iteration number.