用状态空间模型实现精准薄边检测，速度超30帧每秒
EDCSSM: Edge Detection with Convolutional State Space Model
- 通过双输入通道少下采样获取图像状态变量，实现高效记忆与实时学习
- 提出风蚀后处理算法，有效过滤伪边缘并提升边缘精度
- 设计并行计算电路，5K图像处理速度突破30 FPS，适合实时应用
图像边缘检测是计算机图形学中众多复杂任务的基础。由于多层卷积和池化结构导致特征丢失，基于学习的边缘检测模型常产生粗边，难以捕捉小物体边缘。受状态空间模型启发，本文提出一种新算法，通过双输入通道以最小下采样获取图像状态变量，并利用这些状态变量实现实时学习与图像块记忆。为实现精确边缘并抑制伪边缘，设计了名为风蚀的后处理算法处理二值边缘图。为进一步提升算法处理速度，针对计算密集部分设计了并行计算电路，显著提高计算效率。实验结果表明，所提算法在各类图像上均能实现精确的细边定位，并具备噪声抑制能力。借助并行电路，算法在5K图像上的处理速度超过30帧每秒。
原文摘要 · Abstract (English)
Edge detection in images is the foundation of many complex tasks in computer graphics. Due to the feature loss caused by multi-layer convolution and pooling architectures, learning-based edge detection models often produce thick edges and struggle to detect the edges of small objects in images. Inspired by state space models, this paper presents an edge detection algorithm which effectively addresses the aforementioned issues. The presented algorithm obtains state space variables of the image from dual-input channels with minimal down-sampling processes and utilizes these state variables for real-time learning and memorization of image text. Additionally, to achieve precise edges while filtering out false edges, a post-processing algorithm called wind erosion has been designed to handle the binary edge map. To further enhance the processing speed of the algorithm, we have designed parallel computing circuits for the most computationally intensive parts of presented algorithm, significantly improving computational speed and efficiency. Experimental results demonstrate that the proposed algorithm achieves precise thin edge localization and exhibits noise suppression capabilities across various types of images. With the parallel computing circuits, the algorithm to achieve processing speeds exceeds 30 FPS on 5K images.
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