基于时域迭代小波变换的单分子定位图像背景去噪  

Single-Molecule Localization Image Background Denoising Based on Time-Domain Iterative Wavelet Transform

作　　者：吴天琦 肖文 李仁剑 徐以智 胡学娟[2,3] 陈玲玲 Wu Tianqi;Xiao Wen;Li Renjian;Xu Yizhi;Hu Xuejuan;Chen Lingling(College of Health Science and Environmental Engineering,Shenzhen Technology University,Shenzhen,Guangdong 518118,China;College of Physics and Optoelectronic Engineering,Shenzhen University,Shenzhen,Guangdong 518060,China;Key Laboratory of Advanced Optical Precision Manufacturing Technology of Guangdong Provincial Higher Education Institute,Shenzhen,Guangdong 518118,China)

机构地区：[1]深圳技术大学健康与环境工程学院,广东深圳518118 [2]深圳大学物理与光电工程学院,广东深圳518060 [3]广东省普通高校先进光学精密制造技术重点实验室,广东深圳518118

出　　处：《中国激光》2021年第13期166-176,共11页Chinese Journal of Lasers

基　　金：广东省普通高校特色创新类项目(2018KTSCX349);深圳市科技计划基础研究项目(JCYJ 20190813102005655);深圳市科技计划基础研究项目(JCYJ20180301170959233)。

摘　　要：单分子定位显微成像技术采集的图像中包含大量噪声及样本复杂的背景信息,而现有定位重组计算中常用的去噪算法难以去除结构性噪声,从而影响了超分辨图像的重建效果。本文构建了基于时域迭代小波变换(TDIWT)的背景噪声去除算法,该算法可针对不同信噪比的单分子数据集自适应选取合适的分解层数和迭代次数进行批量去噪处理。在模拟数据验证中,所提算法相比空域小波和时间极值发射极恢复算法在结构相似性指数、峰值信噪比上分别高出226%、50.8%和58.5%、16.6%。此外,利用自行搭建的easySTORM系统采集的实验数据和单分子显微成像测试网站提供的实验数据进行了不同算法的背景去噪比较,结果发现,TDIWT处理后重建的超分辨图像可使受噪声影响断裂的微管蛋白呈现为连续状态,验证了其优秀的结构性荧光噪声去除效果。TDIWT算法为单分子显微成像重建过程中结构性背景噪声的去除提供了新的自适应批量处理方案。Objective Although the acquisition of single-molecule localization microscopy(SMLM) includes various noises and background information, completely removal of structural noise is difficult for current denoising algorithms(e.g., the spatial wavelet algorithm and the extreme value-based emitter recovery algorithm) employed in the preprocessing of the reconstruction, thus decreasing the quality of reconstructed super-resolution images. To address this challenge, we develop a new background denoising algorithm based on the time-domain iterative wavelet transform(TDIWT), which can process a batch of SMLM datasets with different signal-to-noise ratios(SNRs) by adaptively selecting the appropriate levels and iterations. This algorithm can provide a new approach for adaptive batching SMLM data structural background.Methods This denoising algorithm based on TDIWT includes two main parts.First,the appropriate level and iteration parameters of TDIWT are adaptively selected for different datasets to balance the time consumption and signal-noise separation effects by calculating the SNR of the dataset.Consequently,time-varied values of each pixel are calculated using TDIWT with selected parameters to separate the signal and background structural noise.The main steps of TDIWT calculation are described by(1)extracting the intensity of the signal from each pixel in a stack in the time domain;(2)acquiring the approximate coefficient via wavelet decomposition and then using it for wavelet reconstruction to fit the background curve;(3)estimating the reconstructed signal that is higher than the background curve and employing wavelet decomposition again;(4)repeating the process until the background fitting data is acquired;(5)outputting separated signal and background image according to the image size(Fig.1).Results and Discussions The simulated results demonstrate that the separation of signal and background using the TDIWT algorithm is more efficient than that using the other denoising algorithms(i.e.,extreme value-based emitter rec

关 键 词：图像处理 单分子定位显微成像 超分辨成像 时域迭代小波变换 背景去噪 

分 类 号：O436[机械工程—光学工程]