A novel approach for partial blur detection and segmentation

This paper proposes a novel approach for partial blur detection and segmentation. The local blur kernels of image blocks are firstly estimated and then a reblurring technique is used to measure relative blur degrees of the local blur kernels. The output of reblurring is a metric to classify blurred and non-blurred image blocks. Furthermore, block-based and pixel-based techniques are incorporated for a fine segmentation of blurred and non-blurred regions. Our approach is evaluated for out-of-focus and motion blurred images. The experimental results show that the proposed approach detects and segments the blurred and non-blurred regions in partial blurred images with 88% accuracy for natural out-of-focus blur, 86% accuracy for artificial out-of-focus blur and 83% accuracy for artificial motion blur, which outperforms the state-of-the-art approaches of partial blur detection and segmentation.