Defocus Blur Detection via Multi-Stream Bottom-Top-Bottom Fully
Convolutional Network
Abstract
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 recurrent reconstruction
network to recurrently 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