A new point spread function estimation approach for recovery of atmospheric turbulence degraded photographs

Images acquired by an optical system are usually degraded by atmospheric turbulence, which exists in the path between the targets and the imaging system. The estimation of atmospheric turbulence degraded Point Spread Function (PSF) without any prior knowledge of clear images is the most challenging and significant technique on image restoration. In this paper, a new PSF estimation approach is proposed for long-exposure atmospheric turbulence degraded images, and is applied to image restoration successfully. The PSF is estimated via an isosceles model which is proposed to approximate one component of the original image's Fourier amplitude. On degraded image restoration, the short-exposure image frames are transformed into a single long-exposure image, and then the restored image is obtained by using the estimated PSF and Wiener Filter. Numerical experiments suggest that this algorithm can obtain accurate PSF from both synthetic and real images. It is also shown objectively that the quality of restored images is greatly enhanced, by applying the Gray Mean Grads and Laplacian Sum standards.