Adaptive Optics Image Restoration via Regularization Priors With Gaussian Statistics

Abstract

In order to compensate for any failure on the use of point spread function (blur kernel) estimation and image estimation priors, we propose a novel regularization priors scheme with adapting the parameter for image restoration involving adaptive optics (AO) images. Our scheme uses a maximum a posteriori estimation with Gaussian statistics on the image and point spread function (blur kernel). An efficient regularization prior method associated with alternating minimization method is described to obtain the optimal solution recursively. Our method is applied to synthetic and real adaptive optics images. After applying our restoration method, satisfying results are obtained. Experimental results demonstrate that our proposed model and method performs better for restoring images in terms of both subjective results and objective assessments than the current state-of-the-art restoring methods. In addition, our proposed method can be a new way to promote their performances for AO image restoration.

Restoration of Blurred and Noisy Images Using Inverse Filtering and Adaptive Threshold Method

“…The introduced method in [13] removes the motion blur based on transformation of the 2-D blurred image into one dimensional (1-D) horizontal blurred vectors. In [14], adaptive optics images are restored using maximum a posteriori estimation with image Gaussian statistics and blur kernel. The proposed scheme in [15] addresses the problem of blind image deconvolution using multiple blurry images algorithm.…”

“…The image degradations may include blurring due to camera motion, for example, [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] or noise which is effectively equivalent to errors in the image pixel values and is due to many causes such as electronic image transmission [19][20][21][22][23][24][25][26][27]. Law enforcement, for example, is an application of image restoration in which the image is blurred due to motion.…”