A Natural Scene Edge Detection Algorithm Based on Image Fusion.

Convolutional neural network (CNN) has been widely used in the edge detection areas and shown competitive results. However, with the increase of receptive fields, the convolution features in CNN gradually become rough and difficult to figure out. To tackle with the problem, a novel network is proposed in this paper, making full use of the multi-scale and multi-level information of the object to perform image-to-image prediction, and combining all distinctive convolution features in a holistic manner. Further, the effect of simply connecting the feature map is enhanced by an image fusion algorithm to improve the utilization of features. The feature maps obtained by convolutions of each layer are fused through the fusion network to obtain a more detailed feature. The improved algorithm is validated in the BSDS500 dataset and the ODS F-measure has reached 0.818, which significantly exceeds the current state-of-the-art results.