Blurred image region detection and segmentation

Estimating both defocus blur and motion blur regions from a single monocular image is a challenging research area in modern image processing and computer vision. Most existing algorithms first divide an image into either non-blur or blur patches. Then blur-type classification is performed on the blur patches only. This means that such approaches include potential risk that incorrect blur region identification may affect the following blur-type classification. In this paper, we present a novel framework for blur region identification to overcome the deficiency of classical methods. We propose a 3-way blur identification method, which divides an image into non-blur, defocus blur, and motion blur regions at once. To this end, we employ intuitive and powerful features based on specific criteria well-suited for our 3-way classification problem. We also take a coarse-to-fine technique to produce pixelwise segmentation results. Experimental results demonstrate that our proposed method outperforms the recent algorithms.