Multiscale spatially-varying blur detection and extraction

Motion blur is caused by the relative motions between the camera and the objects. Most of the existing deblurring algorithms focus on the uniform motion blur for the entire image. However, this assumption generally does not hold in the real world. This means that the task of deblurring needs to involve segmentation of the image into regions with different blurs. In this paper, we present an algorithm on multiscale spatially-varying blur detection and extraction. Firstly, the singular value decom-position (SVD) is performed in multiscale images. For each scale, a robust singular value feature is selected as the local blur characteristic. Then, a more accurate blur distribution map is calculated by normalization and fusion for each pixel. Finally, the input image is segmented into blur/clear regions combined with morphological filtering automatically. The algorithm is tested on the local motion blurred natural image datasets, the results show our method is highly consistent with the human subjective segmentation results.