Existing defocus blur detection (DBD) methods usually explore multi-scale and multi-level features to improve performance. However, defocus blur regions normally have incomplete semantic information, which will reduce DBD's performance if it can't be used properly. In this paper, we address the above problem by exploring deep ensemble networks, where we boost diversity of defocus blur detectors to force the network to generate diverse results that some rely more on high-level semantic information while some ones rely more on low-level information. Then, diverse result ensemble makes detection errors cancel out each other. Specifically, we propose two deep ensemble networks (e.g., adaptive ensemble network (AENet) and encoder-feature ensemble network (EFENet)), which focus on boosting diversity while costing less computation. AENet constructs different light-weight sequential adapters for one backbone network to generate diverse results without introducing too many parameters and computation. AENet is optimized only by the self- negative correlation loss. On the other hand, we propose EFENet by exploring the diversity of multiple encoded features and ensemble strategies of features (e.g., group-channel uniformly weighted average ensemble and self-gate weighted ensemble). Diversity is represented by encoded features with less parameters, and a simple mean squared error loss can achieve the superior performance. Experimental results demonstrate the superiority over the state-of-the-arts in terms of accuracy and speed. Codes and models are available at: https://github.com/wdzhao123/DENets.

Recent state-of-the-art methods on focus region detection (FRD) rely on deep convolutional networks trained with costly pixel-level annotations. In this study, we propose a FRD method that achieves competitive accuracies but only uses easily obtained bounding box annotations. Box-level tags provide important cues of focus regions but lose the boundary delineation of the transition area. A recurrent constraint network (RCN) is introduced for this challenge. In our static training, RCN is jointly trained with a fully convolutional network (FCN) through box-level supervision. The RCN can generate a detailed focus map to locate the boundary of the transition area effectively. In our dynamic training, we iterate between fine-tuning FCN and RCN with the generated pixel-level tags and generate finer new pixel-level tags. To boost the performance further, a guided conditional random field is developed to improve the quality of the generated pixel-level tags. To promote further study of the weakly supervised FRD methods, we construct a new dataset called FocusBox, which consists of 5000 challenging images with bounding box-level labels. Experimental results on existing datasets demonstrate that our method not only yields comparable results than fully supervised counterparts but also achieves a faster speed.