摘要翻译：
本文给出了传感器上神经形态视觉去噪空间滤波器的硬件实现。固定窗口形状的均值或中值空间滤波器以其去噪能力而闻名，但存在使目标边缘模糊的缺点。模糊的效果随着窗口大小的增加而增加。为了保持边缘信息，我们提出了一种自适应空间滤波器，该滤波器利用神经元检测相似像素的能力并计算均值。利用压控振荡器将邻域像素的模拟输入差值转换为脉冲链，作为神经元输入。当输入脉冲向神经元充电到等于或大于其阈值时，神经元就会激发，并且像素被识别为相似的。神经元对像素的响应序列存储在串入并出移位寄存器中。移位寄存器的输出被用作平均电路的选择器开关的输入，使其成为自适应平均操作，从而产生边缘保持平均滤波器。利用来自Caltech数据库的150幅加高斯噪声的图像对该滤波器进行了系统级仿真，以测试该滤波器的鲁棒性和去噪能力。通过调整硬件神经元的阈值，使该边缘保持滤波器在PSNR和MSE方面达到最佳性能，并优于传统的均值和中值滤波器。
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英文标题：
《Neuromorphic adaptive edge-preserving denoising filter》
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作者：
Aidana Irmanova, Olga Krestinskaya, Alex Pappachen James
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最新提交年份：
2017
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  In this paper, we present on-sensor neuromorphic vision hardware implementation of denoising spatial filter. The mean or median spatial filters with fixed window shape are known for its denoising ability, however, have the drawback of blurring the object edges. The effect of blurring increases with an increase in window size. To preserve the edge information, we propose an adaptive spatial filter that uses neuron's ability to detect similar pixels and calculates the mean. The analog input differences of neighborhood pixels are converted to the chain of pulses with voltage controlled oscillator and applied as neuron input. When the input pulses charge the neuron to equal or greater level than its threshold, the neuron will fire, and pixels are identified as similar. The sequence of the neuron's responses for pixels is stored in the serial-in-parallel-out shift register. The outputs of shift registers are used as input to the selector switches of an averaging circuit making this an adaptive mean operation resulting in an edge preserving mean filter. System level simulation of the hardware is conducted using 150 images from Caltech database with added Gaussian noise to test the robustness of edge-preserving and denoising ability of the proposed filter. Threshold values of the hardware neuron were adjusted so that the proposed edge-preserving spatial filter achieves optimal performance in terms of PSNR and MSE, and these results outperforms that of the conventional mean and median filters.
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PDF链接：
https://arxiv.org/pdf/1709.08182