A Spectral and Spatial Approach of Coarse-to-Fine Blurred Image Region Detection



Publication Details

Tang, C., Wu, J., Hou, Y., Wang, P. & Li, W. (2016). A Spectral and Spatial Approach of Coarse-to-Fine Blurred Image Region Detection. IEEE Signal Processing Letters, 23 (11), 1652-1656.


Blur exists in many digital images, it can be mainly categorized into two classes: defocus blur which is caused by optical imaging systems and motion blur which is caused by the relative motion between camera and scene objects. In this letter, we propose a simple yet effective automatic blurred image region detection method. Based on the observation that blur attenuates high-frequency components of an image, we present a blur metric based on the log averaged spectrum residual to get a coarse blur map. Then, a novel iterative updating mechanism is proposed to refine the blur map from coarse to fine by exploiting the intrinsic relevance of similar neighbor image regions. The proposed iterative updating mechanism can partially resolve the problem of differentiating an in-focus smooth region and a blurred smooth region. In addition, our iterative updating mechanism can be integrated into other image blurred region detection algorithms to refine the final results. Both quantitative and qualitative experimental results demonstrate that our proposed method is more reliable and efficient compared to various state-of-the-art methods

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