Learning Crisp Edge Detector Using Logical Refinement Network
This work addresses the challenge of accurately localizing edges in computer vision, particularly for 3D images, which is an incremental improvement over existing methods.
The paper tackles the problem of thick and blurry edges in deep learning-based edge detection by proposing a logical refinement network that produces crisp binary edge maps without post-processing, achieving outstanding performance on both 2D nuclei images and 3D microscopy images compared to state-of-the-art methods.
Edge detection is a fundamental problem in different computer vision tasks. Recently, edge detection algorithms achieve satisfying improvement built upon deep learning. Although most of them report favorable evaluation scores, they often fail to accurately localize edges and give thick and blurry boundaries. In addition, most of them focus on 2D images and the challenging 3D edge detection is still under-explored. In this work, we propose a novel logical refinement network for crisp edge detection, which is motivated by the logical relationship between segmentation and edge maps and can be applied to both 2D and 3D images. The network consists of a joint object and edge detection network and a crisp edge refinement network, which predicts more accurate, clearer and thinner high quality binary edge maps without any post-processing. Extensive experiments are conducted on the 2D nuclei images from Kaggle 2018 Data Science Bowl and a private 3D microscopy images of a monkey brain, which show outstanding performance compared with state-of-the-art methods.