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.

翻译：受限于编码器-解码器架构，基于学习的边缘检测器通常难以预测同时满足正确性和清晰度的边缘图。随着扩散概率模型（DPM）的最新成功，我们发现该模型尤其适用于精确且清晰的边缘检测，因为去噪过程直接应用于原始图像尺寸。因此，我们提出了首个用于通用边缘检测任务的扩散模型，称为DiffusionEdge。为避免高计算资源开销同时保持最终性能，我们将DPM应用于潜空间，并采用经典的交叉熵损失函数（该损失在像素级别具有不确定性感知能力），通过蒸馏方式直接优化潜空间中的参数。我们还采用解耦架构加速去噪过程，并提出相应的自适应傅里叶滤波器来调整特定频率的潜特征。借助所有技术设计，DiffusionEdge可在有限资源下稳定训练，使用更少的增强策略预测清晰且准确的边缘图。在四个边缘检测基准上的大量实验证明了DiffusionEdge在正确性和清晰度方面的优越性。在NYUDv2数据集上，与第二名相比，我们分别将ODS、OIS（无后处理）和AC提升了30.2%、28.1%和65.1%。代码：https://github.com/GuHuangAI/DiffusionEdge。