用梯度能量张量替代结构张量，实现更快的图像处理。
On the Choice of Tensor Estimation for Corner Detection, Optical Flow and Denoising
- 以梯度能量张量替代传统结构张量，提升计算效率。
- 在GPU上实现实时图像增强，帧率提升40%且画质不变。
- 适合需要高速处理的视觉应用，如实时视频处理。
许多图像处理方法，如角点检测、光流计算和迭代增强，均依赖于图像张量。通常这些张量通过结构张量估计。本文表明，在多种场景下，梯度能量张量可作为结构张量的替代方案。我们将该方法应用于角点检测、光流计算和图像增强等常见任务。实验结果表明，梯度能量张量可在GPU上实现实时张量基础图像增强，帧率提升40%且不损失图像质量。
原文摘要 · Abstract (English)
Many image processing methods such as corner detection, optical flow and iterative enhancement make use of image tensors. Generally, these tensors are estimated using the structure tensor. In this work we show that the gradient energy tensor can be used as an alternative to the structure tensor in several cases. We apply the gradient energy tensor to common image problem applications such as corner detection, optical flow and image enhancement. Our experimental results suggest that the gradient energy tensor enables real-time tensor-based image enhancement using the graphical processing unit (GPU) and we obtain 40% increase of frame rate without loss of image quality.
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