Defocus blur detection via edge pixel DCT feature of local patches

Abstract

In common natural image blur, objects that not lie in the focal length of a digital camera generate defocus areas in the photographed image. In this paper, we propose a novel edge-based method for spatially varying defocus blur detection based on reblurred DCT coefficients ratios of the corresponding local patches. This method selects appropriate local reblur scales while detecting the edge points to deal with the problem of different blur degree and texture richness of the local image blocks. A sub-band fusion method of DCT coefficients is proposed to expand the difference between DCT features of in-focus and out-of-focus regions. Edge points blur maps are computed in multi-scale and multi-orientation image windows and more blur points are added to initialize sparse blur maps, finally Matting Laplacian method is used along with multi-scale fusion algorithm to obtain a more accurate blur segmentation. Experimental results present the proposed method has strong advantages in image detail processing and outperforms state-of-the-art methods for blur detection.