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.

中文翻译：

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从原始拜耳模式图像中提取基于梯度的特征

在本文中，研究了去马赛克对梯度提取的影响，并提出了一种基于原始拜耳模式图像的基于梯度的特征提取管道。从理论上和实验上都表明，只要滤色器阵列 (CFA) 图案的排列与梯度算子相匹配，拜耳图案图像适用于基于中心差梯度的特征提取算法，性能下降可以忽略不计。广泛用于各种去马赛克算法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。实验结果表明，从拜耳模式图像中提取的梯度足够稳健，可用于基于定向梯度直方图 (HOG) 的行人检测算法和基于平移不变特征变换 (SIFT) 的匹配算法。通过跳过图像信号处理 (ISP) 流水线中的大部分步骤，可以显着降低计算机视觉系统的计算复杂度和功耗。