Deep Faster Detection of Faint Edges in Noisy Images

Detection of faint edges in noisy images is a challenging problem studied in the last decades. \cite{ofir2016fast} introduced a fast method to detect faint edges in the highest accuracy among all the existing approaches. Their complexity is nearly linear in the image's pixels and their runtime is seconds for a noisy image. By utilizing the U-net architecture \cite{unet}, we show in this paper that their method can be dramatically improved in both aspects of run time and accuracy. By training the network on a dataset of binary images, we develop a method for faint edge detection that works in a linear complexity. Our runtime on a noisy image is milliseconds on a GPU. Even though our method is orders of magnitude faster, we still achieve higher accuracy of detection under many challenging scenarios.