通过邻域线性嵌入实现荧光显微镜去噪

IF 0.9 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Cagatay Kirmiziay, Burhan Aydeniz, Mehmet Turkan
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引用次数: 0

摘要

生物结构荧光成像研究的难题之一是存在噪声破坏。尽管软硬件相关技术在不断进步,但在荧光显微成像中仍不可避免地会出现泊松-高斯混合型噪声。要从荧光图像中提取有价值的信息用于各种生物分析,就必须降低这种噪声。因此,本研究为荧光显微镜引入了一种新的、基于高效学习的去噪方法。所提出的方法主要基于补丁空间的无噪声和噪声子曲面结构之间的线性变换,并从局部图像补丁的线性邻域嵌入中获益。根据视觉和统计结果,所开发的 "邻域线性嵌入去噪 "算法与文献中用于荧光显微图像去噪的其他算法相比,具有很强的竞争力和普遍优越的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fluorescence Microscopy Denoizing via Neighbor Linear Embedding
One of the di ffi culties in studying fl uorescence imaging of biological structures is the presence of noise corruption. Even though hardware-and software-related technologies have undergone continual improvement, the unavoidable e ff ect of Poisson–Gaussian mixture type is generally encountered in fl uorescence microscopy images. This noise should be mitigated to allow the extraction of valuable information from fl uorescence images for various types of biological analysis. Thus, this study introduces a new and e ffi cient learning-based denoizing approach for fl uorescence microscopy. The proposed approach is based mainly on linear transformations between noise-free and noisy submanifold structures of patch spaces, bene fi ting from linear neighbor embeddings of local image patches. According to visual and statistical results, the developed algorithm called "neighbor linear-embedding denoizing" algorithm has a highly competitive and generally superior performance in comparison with the other algorithms used for fl uorescence microscopy image denoizing in the literature
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来源期刊
Electrica
Electrica Engineering-Electrical and Electronic Engineering
CiteScore
2.10
自引率
0.00%
发文量
59
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