Image deblurring by exploiting inherent bi-level regions

In this paper, we propose an image restoration framework for restoring an image degraded by unknown motion blur. Our approach takes advantage of inherent bi-level regions of an image to estimate a blur kernel. The framework contains three parts: bi-level region searching, initial blur kernel estimation and iterative maximum a posteriori (MAP) image restoration. Firstly, candidate bi-level regions are located around the detected corners. We use four image features to score each region and choose the best N regions for estimating an initial blur kernel. Finally, an alternating minimization algorithm is developed to iteratively refine both the blur kernel and the restored image. Experimental results of synthetic and real blurred images are shown to demonstrate the performance of the proposed algorithm.