降噪
计算机科学
人工智能
图像质量
学习迁移
噪音(视频)
模式识别(心理学)
监督学习
计算机视觉
图像(数学)
机器学习
人工神经网络
作者
Yina Wang,Henry Pinkard,Emaad Khwaja,Shuqin Zhou,Laura Waller,Bo Huang
出处
期刊:Optics Express
[Optica Publishing Group]
日期:2021-11-08
卷期号:29 (25): 41303-41303
被引量:19
摘要
When using fluorescent microscopy to study cellular dynamics, trade-offs typically have to be made between light exposure and quality of recorded image to balance the phototoxicity and image signal-to-noise ratio. Image denoising is an important tool for retrieving information from dim cell images. Recently, deep learning based image denoising is becoming the leading method because of its promising denoising performance, achieved by leveraging available prior knowledge about the noise model and samples at hand. We demonstrate that incorporating temporal information in the model can further improve the results. However, the practical application of this method has seen challenges because of the requirement of large, task-specific training datasets. In this work, we addressed this challenge by combining self-supervised learning with transfer learning, which eliminated the demand of task-matched training data while maintaining denoising performance. We demonstrate its application in fluorescent imaging of different subcellular structures.
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