Papers › Defocus Blur Detection via Multi-Stream Bottom-Top-Bottom Fully Convolutional Network
Defocus Blur Detection via Multi-Stream Bottom-Top-Bottom Fully Convolutional Network
Wenda Zhao, Fan Zhao, Dong Wang, Huchuan Lu
Defocus blur detection (DBD) is the separation of infocus and out-of-focus regions in an image. This process has been paid considerable attention because of its remarkable potential applications. Accurate differentiation of homogeneous regions and detection of low-contrast focal regions, as well as suppression of background clutter, are challenges associated with DBD. To address these issues, we propose a multi-stream bottom-top-bottom fully convolutional network (BTBNet), which is the first attempt to develop an end-to-end deep network for DBD. First, we develop a fully convolutional BTBNet to integrate low-level cues and high-level semantic information. Then, considering that the degree of defocus blur is sensitive to scales, we propose multi-stream BTBNets that handle input images with different scales to improve the performance of DBD. Finally, we design a fusion and recursive reconstruction network to recursively refine the preceding blur detection maps. To promote further study and evaluation of the DBD models, we construct a new database of 500 challenging images and their pixel-wise defocus blur annotations. Experimental results on the existing and our new datasets demonstrate that the proposed method achieves significantly better performance than other state-of-the-art algorithms.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Defocus Estimation | CUHK - Blur Detection Dataset | BTBNet (3S + FNet + RRNet) | F-measure | 0.867 | #4 of 5 | Archive leaderboard | report |
| Defocus Estimation | CUHK - Blur Detection Dataset | BTBNet (3S + FNet + RRNet) | MAE | 0.107 | #4 of 5 | Archive leaderboard | report |
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