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论文类型：会议论文

发表时间：2019-01-01

收录刊物：EI、CPCI-S

卷号：2019-June

页面范围：8897-8905

摘要：Defocus blur detection (DBD) is a fundamental yet challenging topic, since the homogeneous region is obscure and the transition from the focused area to the unfocused region is gradual. Recent DBD methods make progress through exploring deeper or wider networks with the expense of high memory and computation. In this paper, we propose a novel learning strategy by breaking DBD problem into multiple smaller defocus blur detectors and thus estimate errors can cancel out each other. Our focus is the diversity enhancement via cross -ensemble network. Specifically, we design an end-to -end network composed of two logical parts: feature extractor network (FENet) and defocus blur detector cross -ensemble network (DBD-CENet). FENet is constructed to extract low-level features. Then the features are fed into DBD-CENet containing two parallel-branches for learning two groups of defocus blur detectors. For each individual, we design cross -negative and self-negative correlations and an error function to enhance ensemble diversity and balance individual accuracy. Finally, the multiple defocus blur detectors are combined with a uniformly weighted average to obtain the final DBD map. Experimental results indicate the superiority of our method in terms of accuracy and speed when compared with several state-of-the-art methods.