A ringing effect often occurs in restored images, and its typical feature is that there are Gibbs-like oscillations in the
neighboring areas of an image with sharp gray scale variations. The existence of the ringing effect causes it to be
difficult for the restored images to be subsequently processed and some image quality evaluation methods to be invalid.
In this paper, we studied several image restoration methods for diffraction-degraded remote sensing image: a Wiener
filtering algorithm that is a simple and rapid image restoration algorithm, which is especially suitable for images without
noise and with accurate PSF estimation and a small degree of blurring; an RL (Richardson-Lucy) algorithm that can
gradually improve the image definition with an increase in iterations but the ringing effect becomes more and more
significant; and a TV (total variation) algorithm that is a normalization algorithm based on noise and ringing suppression.
We used multiple parameters to evaluate the restored images, including BDQ (block difference quality), GMG (gray
mean grads), LS (Laplacian operator sum), and LE (large entropy), for which reference image are not required, as well as
PSNR (peak signal noise ratio), SSIM (structural similarity), GRM(Gradient Ringing Metric) and HVSWGM (weighted
gradient metric based on human visual system), for which reference image are required. The results show that the
HVSWGM method is insensitive to the ringing effect occurring in image restoration and the evaluation result is
completely consistent with a subjective evaluation result with a human visual system, and that many non-reference
methods fail completely in assessing restored image with ringing effect, and only BDQ method is able to conform to
subjective evaluation method to some extent.