This paper proposes a novel analog front end to filter edges in captured images. The proposed architecture employs the Sobel algorithm and uses a 3 ×3 kernel to process the input images. We used 180 nm Semi-Conductor Laboratory (SCL) CMOS fabrication technology for implementation with a power supply of 1.8 V. The proposed design employs six analog differential pairs in each horizontal and vertical gradient unit. The inputs to the differential pairs are the active signals fed from the 3 ×3 kernel using the Sobel weight masks. We connected the differential datum pairs in cascade to estimate the gradient index in horizontal/vertical direction using the net difference current between the output terminals. The horizontal and vertical gradient units, viz., $G_H$ and $G_V$, respectively, are configured based on the polarity of weights in respective Sobel masks. The gradient $(G)$ of the processing pixel (PP) is estimated using the net difference current in the combined circuitry employing $G_H$ and $G_V$ cells. The key advantages of the proposed design are very low area realization, high accuracy, and low power dissipation. The efficacy of the proposed edge filter analog front end is estimated through simulations and analysis with two sets of test signals in the processing window.

本文提出了一种新颖的模拟前端来过滤捕获图像中的边缘。所提出的架构采用 Sobel 算法并使用 3 × 3 内核来处理输入图像。我们使用 180 nm 半导体实验室 (SCL) CMOS 制造技术来实现，电源电压为 1.8 V。所提出的设计在每个水平和垂直梯度单元中采用六个模拟差分对。差分对的输入是使用 Sobel 权重掩模从 3 ×3 内核馈送的有源信号。我们将差分数据对级联连接，以使用输出端子之间的净差电流来估计水平/垂直方向的梯度指数。水平和垂直梯度单位，即$G_H$和$G_V$分别根据各个 Sobel 掩模中权重的极性进行配置。梯度$（G）$使用组合电路中的净差电流来估计处理像素（PP）$G_H$和$G_V$细胞。所提出设计的主要优点是实现面积非常小、精度高和功耗低。所提出的边缘滤波器模拟前端的功效是通过在处理窗口中使用两组测试信号进行仿真和分析来估计的。