发表机构
Linköping University; Center for Medical Image Science and Visualization (CMIV)（林雪平大学; 医学影像科学与可视化中心（CMIV））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出梯度能量张量可替代结构张量用于角点检测等图像处理任务，基于GPU实现实时图像增强，在不损失画质的前提下帧率提升40%。
AI 中文摘要
角点检测、光流、迭代增强等诸多图像处理方法均会用到图像张量，这类张量通常采用结构张量进行估计。本研究表明，梯度能量张量可在多种场景下作为结构张量的替代方案，我们将梯度能量张量应用于角点检测、光流、图像增强等常见图像处理任务。实验结果显示，梯度能量张量可借助图形处理单元（GPU）实现基于张量的实时图像增强，且在不损失图像质量的前提下，帧率提升了40%。
英文摘要
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.
Journal refAsian Conference on Computer Vision - ACCV 2014 Workshops, Lecture Notes in Computer Science, vol. 9009, pp. 16-30, Springer, 2015
DOI：10.1007/978-3-319-16631-5_2
