夏皓天, 钱芸生, 王逸伦, 郎怡政. 基于FPGA的低照度条件下EBAPS图像混合噪声去除算法[J]. 应用光学, 2022, 43(6): 1075-1087. DOI: 10.5768/JAO202243.0604006
引用本文: 夏皓天, 钱芸生, 王逸伦, 郎怡政. 基于FPGA的低照度条件下EBAPS图像混合噪声去除算法[J]. 应用光学, 2022, 43(6): 1075-1087. DOI: 10.5768/JAO202243.0604006
XIA Haotian, QIAN Yunsheng, WANG Yilun, LANG Yizheng. Mixed noise removal algorithm of EBAPS image under low illumination condition based on FPGA[J]. Journal of Applied Optics, 2022, 43(6): 1075-1087. DOI: 10.5768/JAO202243.0604006
Citation: XIA Haotian, QIAN Yunsheng, WANG Yilun, LANG Yizheng. Mixed noise removal algorithm of EBAPS image under low illumination condition based on FPGA[J]. Journal of Applied Optics, 2022, 43(6): 1075-1087. DOI: 10.5768/JAO202243.0604006

基于FPGA的低照度条件下EBAPS图像混合噪声去除算法

Mixed noise removal algorithm of EBAPS image under low illumination condition based on FPGA

  • 摘要: 为了解决单一的中值滤波和高斯滤波算法对低照度图像中同时存在的脉冲噪声和泊松噪声抑制效果不佳、边缘细节保护不足的问题,提出一种基于可编程逻辑门阵列(field programmable gate array,FPGA)的开关中值-高斯融合(open and close mix-median-Gaussian,OCMMG)滤波算法。首先,利用最小四方向差值检测每个像素点的异常程度,根据脉冲噪声判别阈值分配权重,进行第1步滤波处理;然后,利用四方向边缘检测算法提取图像边缘,根据设置的边缘置信度表征值进行第2步滤波处理;最后,用电子轰击有源像素传感器(electron bombarded active pixel sensor,EBAPS)在1×10−3 lx照度条件下采集的图像,基于FPGA对其进行实时图像处理。实验结果表明,FPGA处理结果与软件仿真处理结果相符。该算法相比于中值滤波和高斯滤波算法,峰值信噪比分别提高了3.23%和16.34%,结构相似性分别提高了14.66%和33.86%,边缘保持指数分别提高了0.49%和4.21%,能够有效去除EBAPS图像的混合噪声,并满足实时性要求。

     

    Abstract: In order to solve the problem that single median filtering and gaussian filtering algorithm is not effective in suppressing impulse noise and poisson noise simultaneously in low illumination image, and the edge detail protection is insufficient, an open and close mix-median-gaussian (OCMMG) filtering algorithm based on field programmable gate array (FPGA) was proposed. Firstly, the minimum four-direction difference was used to detect the anomaly degree of each pixel point, the weight was allocated according to the threshold of pulse noise discrimination, and the first step was filtering. Then, the four-direction edge detection algorithm was used to extract image edges, and the second step was filtered according to the set edge confidence characterization value. Finally, the images collected by electron bombarded active pixel sensor (EBAPS) under the condition of 1×10−3 lx illumination were processed by FPGA in real time. The experimental results show that the FPGA processing results are consistent with the software simulation processing results. Compared with the median filtering and gaussian filtering algorithm, the peak signal-to-noise ratio (PSNR) of the algorithm is improved by 3.23% and 16.34%, the structural similarity is improved by 14.66% and 33.86%, and the edge retention index is improved by 0.49% and 4.21%, respectively, which can effectively remove the mixed noise of EBAPS image and meet the real-time requirements.

     

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