National Research Council (CNR), Institute of Applied Sciences and Intelligent Systems, Napoli, Italy.
Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, New York, USA.
J Biophotonics. 2022 Jun;15(6):e202100379. doi: 10.1002/jbio.202100379. Epub 2022 Apr 1.
In the literature of SRS microscopy, the hardware characterization usually remains separate from the image processing. In this article, we consider both these aspects and statistical properties analysis of image noise, which plays the vital role of joining links between them. Firstly, we perform hardware characterization by systematic measurements of noise sources, demonstrating that our in-house built microscope is shot noise limited. Secondly, we analyze the statistical properties of the overall image noise, and we prove that the noise distribution can be dependent on image direction, whose origin is the use of a lock-in time constant longer than pixel dwell time. Finally, we compare the performances of two widespread general algorithms, that is, singular value decomposition and discrete wavelet transform, with a method, that is, singular spectrum analysis (SSA), which has been adapted for stimulated Raman scattering images. In order to validate our algorithms, in our investigations lipids droplets have been used and we demonstrate that the adapted SSA method provides an improvement in image denoising.
在 SRS 显微镜的文献中，硬件特性描述通常与图像处理分开。在本文中，我们考虑了这两个方面以及图像噪声的统计特性分析，这在它们之间起着连接的重要作用。首先，我们通过对噪声源的系统测量来进行硬件特性描述，证明了我们内部构建的显微镜是受散粒噪声限制的。其次，我们分析了整体图像噪声的统计特性，并证明噪声分布可能与图像方向有关，其原因是使用了比像素停留时间更长的锁定时间常数。最后，我们将两种广泛使用的通用算法，即奇异值分解和离散小波变换，与一种已适用于受激拉曼散射图像的方法，即奇异谱分析（SSA）进行了比较。为了验证我们的算法，我们在研究中使用了脂质液滴，并证明了适应后的 SSA 方法在图像去噪方面有了改进。
