Image-Scale-Symmetric Cooperative Network for Defocus Blur Detection
Fan Zhao, Huimin Lu, Wenda Zhao, Libo Yao
Abstract
Defocus blur detection (DBD) for natural images is a challenging vision task especially in the presence of homogeneous regions and gradual boundaries. In this paper, we propose a novel image-scale-symmetric cooperative network (IS2CNet) for DBD. On one hand, in the process of image scales from large to small, IS2CNet gradually spreads the recept of image content. Thus, the homogeneous region detection map can be optimized gradually. On the other hand, in the process of image scales from small to large, IS2CNet gradually feels the high-resolution image content, thereby gradually refining transition region detection. In addition, we propose a hierarchical feature integration and bi-directional delivering mechanism to transfer the hierarchical feature of previous image scale network to the input and tail of the current image scale network for guiding the current image scale network to better learn the residual. The proposed approach achieves state-of-the-art performance on existing datasets. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Codes and results are available at: <uri>https://github.com/wdzhao123/IS2CNet</uri></i> .