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A method of eliminating the signal-dependent random noise from the raw CMOS image sensor data based on Kalman filter

机译:一种基于卡尔曼滤波的CMOS图像传感器原始数据中与信号无关的随机噪声消除方法

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摘要

Many conventional denoising solutions adopt the channel-dependent noise model, which is less suitable than signal-dependent noise model to describe the noise characteristic of CMOS image sensor. In this paper, the cascaded Kalman filter is designed to eliminate the signal-dependent random noise from the raw data of CMOS image sensor. The major contribution of this work is that it establishes the state equation corresponding to the circuit structure of sensor pixel, instead of designing the denoising algorithm only from a mathematical point of view. The experimental results confirm that the performance of the proposed method outperforms the conventional ones.
机译:许多传统的去噪解决方案采用与通道有关的噪声模型,该模型不如与信号相关的噪声模型更适合描述CMOS图像传感器的噪声特性。本文设计了级联卡尔曼滤波器,以从CMOS图像传感器的原始数据中消除依赖信号的随机噪声。这项工作的主要贡献在于,它建立了与传感器像素的电路结构相对应的状态方程,而不是仅从数学角度设计去噪算法。实验结果证明,该方法的性能优于传统方法。

著录项

  • 来源
    《Signal processing》 |2014年第11期|401-406|共6页
  • 作者单位

    Institute of Electronic and Information, Hangzhou Dianzi University, Hangzhou, PR China;

    Institute of Electronic and Information, Hangzhou Dianzi University, Hangzhou, PR China;

    School of Electronic and Information Engineering, Tianjin University, Tianjin, PR China;

    School of Electronic and Information Engineering, Tianjin University, Tianjin, PR China;

    Institute of Electronic and Information, Hangzhou Dianzi University, Hangzhou, PR China;

    Brigates Microelectronics Co., Ltd., Kunshan, PR China;

    Brigates Microelectronics Co., Ltd., Kunshan, PR China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Signal-dependent random noise; Denoising; Kalman filter; CMOS image sensor;

    机译:信号相关的随机噪声;去噪;卡尔曼滤波器CMOS图像传感器;

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