Gradient-Based Feature Extraction From Raw Bayer Pattern Images
Gradient-Based Feature Extraction From Raw Bayer Pattern Images
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DOI：
10.1109/tip.2021.3067166
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发表时间：
2020-04
影响因子：
10.6
通讯作者：
Wei Zhou;Ling Zhang;Shengyu Gao;Xin Lou
中科院分区：
文献类型：
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作者：
Wei Zhou;Ling Zhang;Shengyu Gao;Xin Lou
本文研究了去马赛克对梯度提取的影响，提出了一种基于原始拜耳图案图像的梯度特征提取管道。理论和实验都表明，只要彩色滤波阵列（CFA）模式的排列与梯度算子匹配，Bayer模式图像适用于基于中心差分梯度的特征提取算法，性能下降可以忽略不计。将各种去马赛克算法中广泛使用的色差恒常性假设应用于所提出的基于拜耳模式图像的梯度提取管道中。实验结果表明，从拜耳模式图像中提取的梯度具有足够的鲁棒性，可用于基于定向梯度直方图（HOG）的行人检测算法和基于移位不变特征变换（SIFT）的匹配算法。通过跳过图像信号处理（ISP）管道中的大部分步骤，可以显着降低计算机视觉系统的计算复杂度和功耗。
In this paper, the impact of demosaicing on gradient extraction is studied and a gradient-based feature extraction pipeline based on raw Bayer pattern images is proposed. It is shown both theoretically and experimentally that the Bayer pattern images are applicable to the central difference gradient-based feature extraction algorithms with negligible performance degradation, as long as the arrangement of color filter array (CFA) patterns matches the gradient operators. The color difference constancy assumption, which is widely used in various demosaicing algorithms, is applied in the proposed Bayer pattern image-based gradient extraction pipeline. Experimental results show that the gradients extracted from Bayer pattern images are robust enough to be used in histogram of oriented gradients (HOG)-based pedestrian detection algorithms and shift-invariant feature transform (SIFT)-based matching algorithms. By skipping most of the steps in the image signal processing (ISP) pipeline, the computational complexity and power consumption of a computer vision system can be reduced significantly.