Canny-Net: Known Operator Learning for Edge Detection

We present Canny-Net, a modification of the 2D Canny edge detector into a pre-weighted neural network. CT scans of objects that contain metal components are characterized by artifacts like beam hardening, total absorption and scatter. Edge detection with classical methods, e.g. the Canny edge detector, is therefore prone to error. Using known operator learning, Canny-Net reduces maximum error bounds for edge detection in such CT scans. We show that Canny-Net is a light-weight neural network with a small number of trainable parameters. After training, we observe an increase of 11% in F1 score on 2304 test images when compared to the Canny edge detector. Due to its adaptability and low computational cost, Canny-Net can be considered for a wide range of different applications.

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