Image Deblurring Based on Spectral Measures of Whiteness

ABSTRACT Image Deblurring is an ill-posed inverse problem used to reconstruct the sharp image from the unknown blurred image. This process involves restoration of high frequency information from the blurred image. It includes a learning technique which initially focuses on the main edges of the image and then gradually takes details into account. As blind image deblurring is ill-posed, it has infinite number of solutions leading to an ill-conditioned blur operator. So regularization or prior knowledge on both the unknown image and the blur operator is needed to address this problem. The performance of this optimization problem depends on the regularization parameter and the iteration number. In already existing methods the iterations have to be manually stopped. In this paper, a new idea is proposed to regulate the number of iterations and the regularization parameter automatically. The proposed criteria yields, on average, an ISNR only 0.38dB below what is obtained by manual stopping. The results obtained with synthetically blurred images are good and considerable, even when the blur operator is ill-conditioned and the blurred image is noisy. KEYWORDS Image deconvolution, blind image deblurring, Image restoration, whiteness measure