Low Cost Robust Blur Estimator

In this paper a novel local blur estimation method is presented. The focal blur process is usually modeled as a Gaussian low-pass filtering and then the problem of blur estimation is to identify the Gaussian blur kernel. In the proposed method, the blurred input image is first re-blurred by Gaussian blur kernels with different blur radii. Then the difference ratios between the multiple re-blurred images and the input image are used to determine the unknown blur radius. We show that the proposed method does not require edge detection preprocessing and can estimate a wide range of blur radius. Experimental results of the proposed method on both synthetic and natural images and a comparison with a state-of-the-art method are presented.