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DiffusionEdge: Diffusion Probabilistic Model for Crisp Edge Detection (2401.02032v2)

Published 4 Jan 2024 in cs.CV

Abstract: 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.

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Citations (16)
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Summary

  • The paper presents DiffusionEdge, a diffusion probabilistic model that accurately detects crisp edges by denoising at the original image scale.
  • It introduces key innovations including a decoupled architecture, adaptive Fourier filtering, and uncertainty distillation to enhance edge accuracy and efficiency.
  • Experimental results on benchmarks like NYUDv2 show significant improvements in F-scores and Average Crispness, reducing heavy reliance on post-processing techniques.

Analysis of "DiffusionEdge: Diffusion Probabilistic Model for Crisp Edge Detection"

The research paper entitled "DiffusionEdge: Diffusion Probabilistic Model for Crisp Edge Detection" presents an innovative approach to addressing the challenges inherent in edge detection, a critical computer vision task. The authors propose a novel method that leverages the potential of Diffusion Probabilistic Models (DPMs) to achieve both correct and crisp edge maps more efficiently than prevailing techniques.

Key Contributions

The core contribution of the paper is the introduction of DiffusionEdge, the first instance of employing a diffusion model for edge detection tasks. Traditional edge detection systems, including both classical methods like Canny and modern CNN-based techniques, often struggle with balancing edge correctness with edge crispness. The proposed DiffusionEdge solves this problem by harnessing the strengths of DPMs, notably their ability to perform denoising operations directly on the original image scale.

Furthermore, the paper introduces several pertinent technical innovations to enhance the capabilities of DiffusionEdge:

  1. Decoupled Architecture: By incorporating a decoupled diffusion architecture, similar to that used in DDM, the method accelerates inference processes significantly.
  2. Adaptive Fourier Filter: The introduction of an adaptive frequency filtering mechanism allows the adjustment of specific frequency components, thereby refining latent features crucial for achieving crisp edge detection.
  3. Uncertainty Distillation: This approach retains essential uncertainty information inherent in datasets labeled by multiple annotators. The method optimizes parameters directly in the latent space using a cross-entropy loss, an approach that reduces both computational demands and the need for extensive data augmentation.

The efficacy of DiffusionEdge is demonstrated through extensive experiments across four edge detection benchmarks: BSDS, NYUDv2, Multicue, and BIPED. The results indicate that DiffusionEdge outperforms contemporary state-of-the-art approaches in terms of F-scores and Average Crispness (AC), particularly on the NYUDv2 dataset with significant improvements of 30.2% in ODS, 28.1% in OIS, and 65.1% in AC compared to the second-best method.

Implications and Future Directions

The development of DiffusionEdge presents substantial implications for both theoretical understanding and practical applications in edge detection. By integrating a diffusion model with a novel adaptive filtering technique and uncertainty-aware optimization, this work sets a precedent for future explorations into using generative models for edge detection tasks. This is particularly relevant in scenarios where edge crispness—indispensable for tasks such as 2D perception, image generation, and 3D reconstruction—is of high importance.

Practically, the reduction in reliance on computationally expensive post-processing techniques and extensive dataset augmentations marks a significant stride towards more efficient edge detection frameworks. This method's success also hints at the potential application of diffusion models in other areas of computer vision beyond generative tasks, such as object segmentation and recognition.

However, as noted by the authors, the efficiency of the diffusion model in terms of inference speed still warrants further investigation. Future research should focus on optimization strategies that maintain accuracy and crispness while minimizing computational overhead.

In summary, the "DiffusionEdge" model illustrates a meaningful step forward in edge detection research, prompting further discourse on the integration of diffusion probabilistic models in vision tasks and sparking ideas for optimizing computational performances in these systems.

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