• Journal of Broadcast Engineering
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• v.25 no.1
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• pp.58-66
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• 2020

The blur effect, which is generated by various external factors such as out-of-focus and object movement, degrades high-frequency components in the original sharp image. Based on this observation, we propose a novel method for blur detection using textural features. Specifically, the proposed method simultaneously adopts learning-based and watershed-based textural features, which effectively detect the blur on various situations. Moreover, we employ the region-based refinement to improve the processing time while also increasing detection accuracy. Experimental results demonstrate that the proposed method provides the competitive performance compared to previous approaches in literature.