A U-Shaped Multi-Scale Network for Defocus Blur Detection

Defocus blur detection (DBD) aims at accurately extract the in-focus sharp regions in natural images. Despite significant progress, the presence of uniform regions and gradient boundaries remain two of the most challenging problems. In this paper, we use a U-shaped multi-scale image input network for image DBD. The model is a U-shaped encoder-decoder structured network that incorporates inputs from multi-scale images to simultaneously extract features from images of different scales, and a feature fusion module (FFM) of the corresponding scales in the encoder part in order to better learn the features of these different scales. Experiments on two public datasets demonstrate the superior performance of the method in this paper in dealing with defocus blur detection.