Bi-l0-l2-Norm Regularization for Blind Motion Deblurring
This addresses the problem of improving image quality in blind motion deblurring for applications like photography and computer vision, representing an incremental advance.
The paper tackles blind motion deblurring by proposing a bi-l0-l2-norm regularization method for estimating motion blur-kernels, resulting in more accurate kernels and better restored images compared to existing methods, with competitive performance on benchmark and real-world datasets.
In blind motion deblurring, leading methods today tend towards highly non-convex approximations of the l0-norm, especially in the image regularization term. In this paper, we propose a simple, effective and fast approach for the estimation of the motion blur-kernel, through a bi-l0-l2-norm regularization imposed on both the intermediate sharp image and the blur-kernel. Compared with existing methods, the proposed regularization is shown to be more effective and robust, leading to a more accurate motion blur-kernel and a better final restored image. A fast numerical scheme is deployed for alternatingly computing the sharp image and the blur-kernel, by coupling the operator splitting and augmented Lagrangian methods. Experimental results on both a benchmark image dataset and real-world motion blurred images show that the proposed approach is highly competitive with state-of-the- art methods in both deblurring effectiveness and computational efficiency.