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：显微图像常受到高噪声水平的干扰，这阻碍了后续分析与解读。已有研究提出内容感知图像复原（CARE）方法解决该问题，但此类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，本文提出一种新颖的少样本显微图像去噪框架。该方法融合了通过对比学习（CL）训练的生成对抗网络（GAN）与两项结构保持损失项（结构相似性指数和全变分损失），从而在少量数据条件下进一步提升去噪图像质量。我们在三个公认的显微成像数据集上验证了方法的有效性，结果表明：该方法能大幅减少训练数据需求，同时保持去噪质量，从而减轻成对数据获取的负担，并实现少样本学习。该框架可轻松扩展至其他图像复原任务，具有显著推动显微图像分析领域发展的潜力。