Zieger Silvia E, Jones Peter D, Koren Klaus
Aarhus University Centre for Water Technology (WATEC), Department of Biology, Section for Microbiology, Aarhus University, 8000, Aarhus C, Denmark.
NMI Natural and Medical Sciences Institute at the University of Tübingen, 72770, Reutlingen, Germany.
ACS Omega. 2022 Mar 28;7(14):11829-11838. doi: 10.1021/acsomega.1c07232. eCollection 2022 Apr 12.
Optical chemical imaging has established itself as a valuable technique for visualizing analyte distributions in 2D, notably in medical, biological, and environmental applications. In particular for image acquisitions on small scales between few millimeter to the micrometer range, as well as in heterogeneous samples with steep analyte gradients, image resolution is essential. When individual pixels are inspected, however, image noise becomes a metric as relevant as image accuracy and precision, and denoising filters are applied to preserve relevant information. While denoising filters smooth the image noise, they can also lead to a loss of spatial resolution and thus to a loss of relevant information about analyte distributions. To investigate the trade-off between image resolution and noise reduction for information preservation, we studied the impact of random camera noise and noise due to incorrect camera settings on oxygen optodes using the ratiometric imaging technique. First, we estimated the noise amplification across the calibration process using a Monte Carlo simulation for nonlinear fit models. We demonstrated how initially marginal random camera noise results in a significant standard deviation (SD) for oxygen concentration of up to 2.73% air under anoxic conditions, although the measurement was conducted under ideal conditions and over 270 thousand sample pixels were considered during calibration. Second, we studied the effect of the Gaussian denoising filter on a steep oxygen gradient and investigated the impact when the smoothing filter is applied during data processing. Finally, we demonstrated the effectiveness of a Savitzky-Golay filter compared to the well-established Gaussian filter.
光学化学成像已成为一种有价值的技术，可用于二维可视化分析物分布，特别是在医学、生物学和环境应用中。尤其对于在几毫米到微米范围内的小尺度图像采集，以及在具有陡峭分析物梯度的异质样品中，图像分辨率至关重要。然而，当检查单个像素时，图像噪声成为与图像准确性和精度同样相关的指标，并且应用去噪滤波器来保留相关信息。虽然去噪滤波器可以平滑图像噪声，但它们也可能导致空间分辨率的损失，从而导致关于分析物分布的相关信息丢失。为了研究图像分辨率与降噪以保留信息之间的权衡，我们使用比率成像技术研究了随机相机噪声和由于相机设置不正确导致的噪声对氧光学传感器的影响。首先，我们使用蒙特卡罗模拟对非线性拟合模型估计了整个校准过程中的噪声放大。我们证明了，尽管测量是在理想条件下进行的，并且在校准过程中考虑了超过27万个样本像素，但最初微不足道的随机相机噪声在缺氧条件下会导致氧浓度的标准偏差（SD）高达2.73%空气。其次，我们研究了高斯去噪滤波器对陡峭氧梯度的影响，并研究了在数据处理过程中应用平滑滤波器时的影响。最后，我们证明了Savitzky-Golay滤波器与成熟的高斯滤波器相比的有效性。
