Motion deblurring using edge map with blurred/noisy image pairs

Motion deblurring methods using blurred/noisy image pairs usually include denoising process of the noisy image. Because both remaining noise and distorted fine details in the denoised image cause an error on deblurring, we propose an algorithm using an edge map of the noisy image to retain sharp edge information while neglecting noise in any smooth region that does not contain information about the motion that occurred during the exposure. In addition, the blur kernel is efficiently estimated by employing the fast total variation regularization method for the gradients of blurred and noisy images only on edge regions. For latent image restoration, another fidelity term is added, which compares the gradients of the noisy and estimated latent images on edge regions to preserve the fine details of the noisy image. To model a sparse distribution of real-world image gradients, a deconvolution method imposing hyper-Laplacian priors based on an alternating minimization scheme is also derived to restore a latent image efficiently. Experimental results show that the peak signal-to-noise ratios of the restored images against the original latent images have been increased by 11.1% on average, when compared to the existing algorithms using an image pair.