Microscopy images are crucial for life science research, allowing detailed inspection and characterization of cellular and tissue-level structures and functions. However, microscopy data are unavoidably affected by image degradations, such as noise, blur, or others. Many such degradations also contribute to a loss of image contrast, which becomes especially pronounced in deeper regions of thick samples. Today, best performing methods to increase the quality of images are based on Deep Learning approaches, which typically require ground truth (GT) data during training. Our inability to counteract blurring and contrast loss when imaging deep into samples prevents the acquisition of such clean GT data. The fact that the forward process of blurring and contrast loss deep into tissue can be modeled, allowed us to propose a new method that can circumvent the problem of unobtainable GT data. To this end, we first synthetically degraded the quality of microscopy images even further by using an approximate forward model for deep tissue image degradations. Then we trained a neural network that learned the inverse of this degradation function from our generated pairs of raw and degraded images. We demonstrated that networks trained in this way can be used out-of-distribution (OOD) to improve the quality of less severely degraded images, e.g. the raw data imaged in a microscope. Since the absolute level of degradation in such microscopy images can be stronger than the additional degradation introduced by our forward model, we also explored the effect of iterative predictions. Here, we observed that in each iteration the measured image contrast kept improving while detailed structures in the images got increasingly removed. Therefore, dependent on the desired downstream analysis, a balance between contrast improvement and retention of image details has to be found.

翻译：显微图像对于生命科学研究至关重要，可实现对细胞及组织层级结构与功能的精细观测与表征。然而，显微数据不可避免地受到噪声、模糊等图像退化效应的影响。这类退化中许多还会导致图像对比度损失，这一问题在厚样本深层区域尤为显著。当前性能最优的图像质量提升方法基于深度学习技术，通常需在训练过程中使用真实标注（GT）数据。但当我们对样本深层进行成像时，无法有效抵消模糊与对比度损失，因而难以获取此类纯净的GT数据。鉴于组织深层模糊与对比度损失的成像正向过程具有可建模性，我们据此提出了一种能够规避GT数据获取难题的新方法。具体而言，我们首先利用近似的深层组织图像退化正向模型，人为进一步降低显微图像质量；随后训练神经网络学习该退化函数的逆过程。实验证明，以这种方式训练的网络可被用于分布外（OOD）场景，以提升退化程度较轻的图像（如显微镜原始成像数据）的质量。由于此类显微图像本身的退化程度可能强于我们正向模型引入的附加退化，我们还探究了迭代预测的效果。观察发现，每次迭代中图像实测对比度持续提升，但图像中的精细结构会随之逐渐消失。因此，针对所需的下游分析任务，需在对比度改善与图像细节保留之间寻求平衡。