Microscopy images often suffer from high levels of noise, which can hinder further analysis and interpretation. Content-aware image restoration (CARE) methods have been proposed to address this issue, but they often require large amounts of training data and suffer from over-fitting. To overcome these challenges, we propose a novel framework for few-shot microscopy image denoising. Our approach combines a generative adversarial network (GAN) trained via contrastive learning (CL) with two structure preserving loss terms (Structural Similarity Index and Total Variation loss) to further improve the quality of the denoised images using little data. We demonstrate the effectiveness of our method on three well-known microscopy imaging datasets, and show that we can drastically reduce the amount of training data while retaining the quality of the denoising, thus alleviating the burden of acquiring paired data and enabling few-shot learning. The proposed framework can be easily extended to other image restoration tasks and has the potential to significantly advance the field of microscopy image analysis.

翻译：显微图像常受到高水平噪声的影响，这阻碍了后续的分析与解读。为解决此问题，内容感知图像恢复（CARE）方法被提出，但这些方法通常需要大量训练数据且易出现过拟合。为克服这些挑战，我们提出了一种用于少样本显微图像去噪的新型框架。该方法结合了通过对比学习训练的生成对抗网络（GAN）与两个结构保持损失项（结构相似性指数和全变分损失），以在少量数据下进一步提升去噪图像质量。我们在三个知名的显微成像数据集上验证了该方法的有效性，结果表明，我们能够在大幅减少训练数据量的同时保持去噪质量，从而减轻配对数据获取的负担并实现少样本学习。所提出的框架可轻松扩展到其他图像恢复任务，并有望显著推动显微图像分析领域的发展。