Affiliation:
1. Wuxi University
2. HorizonFlow Laboratory
3. Jiangnan University
4. Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences
5. Shanxi Agricultural University
6. Nanjing Agricultural University
Abstract
Lowering the excitation to reduce phototoxicity and photobleaching while numerically enhancing the fluorescence signal is a useful way to support long-term observation in fluorescence microscopy. However, invalid features, such as near-zero gradient dark backgrounds in fluorescence images, negatively affect the neural networks due to the network training locality. This problem makes it difficult for mature deep learning-based image enhancement methods to be directly extended to fluorescence imaging enhancement. To reduce the negative optimization effect, we previously designed Kindred-Nets in conjunction with a mixed fine-tuning scheme, but the mapping learned from the fine-tuning dataset may not fully apply to fluorescence images. In this work, we proposed a new, to the best of our knowledge, deep low-excitation fluorescence imaging global enhancement framework, named Deep-Gamma, that is completely different from our previously designed scheme. It contains GammaAtt, a self-attention module that calculates the attention weights from global features, thus avoiding negative optimization. Besides, in contrast to the classical self-attention module outputting multidimensional attention matrices, our proposed GammaAtt output, as multiple parameters, significantly reduces the optimization difficulty and thus supports easy convergence based on a small-scale fluorescence microscopy dataset. As proven by both simulations and experiments, Deep-Gamma can provide higher-quality fluorescence-enhanced images compared to other state-of-the-art methods. Deep-Gamma is envisioned as a future deep low-excitation fluorescence imaging enhancement modality with significant potential in medical imaging applications. This work is open source and available at https://github.com/ZhiboXiao/Deep-Gamma.
Funder
Shanghai Sailing Program
Fundamental Research Program of Shanxi Province
National Natural Science Foundation of China
Natural Science Foundation of Jiangsu Province
Wuxi University Research Start-up Fund for Introduced Talents
Subject
Atomic and Molecular Physics, and Optics