  Limited by the encoder-decoder architecture, learning-based edge detectors
usually have difficulty predicting edge maps that satisfy both correctness and
crispness. With the recent success of the diffusion probabilistic model (DPM),
we found it is especially suitable for accurate and crisp edge detection since
the denoising process is directly applied to the original image size.
Therefore, we propose the first diffusion model for the task of general edge
detection, which we call DiffusionEdge. To avoid expensive computational
resources while retaining the final performance, we apply DPM in the latent
space and enable the classic cross-entropy loss which is uncertainty-aware in
pixel level to directly optimize the parameters in latent space in a
distillation manner. We also adopt a decoupled architecture to speed up the
denoising process and propose a corresponding adaptive Fourier filter to adjust
the latent features of specific frequencies. With all the technical designs,
DiffusionEdge can be stably trained with limited resources, predicting crisp
and accurate edge maps with much fewer augmentation strategies. Extensive
experiments on four edge detection benchmarks demonstrate the superiority of
DiffusionEdge both in correctness and crispness. On the NYUDv2 dataset,
compared to the second best, we increase the ODS, OIS (without post-processing)
and AC by 30.2%, 28.1% and 65.1%, respectively. Code:
https://github.com/GuHuangAI/DiffusionEdge.
