Deep learning-based super-resolution models have the potential to revolutionize biomedical imaging and diagnoses by effectively tackling various challenges associated with early detection, personalized medicine, and clinical automation. However, the requirement of an extensive collection of high-resolution images presents limitations for widespread adoption in clinical practice. In our experiment, we proposed an approach to effectively train the deep learning-based super-resolution models using only one real image by leveraging self-generated high-resolution images. We employed a mixed metric of image screening to automatically select images with a distribution similar to ground truth, creating an incrementally curated training data set that encourages the model to generate improved images over time. After five training iterations, the proposed deep learning-based super-resolution model experienced a 7.5\% and 5.49\% improvement in structural similarity and peak-signal-to-noise ratio, respectively. Significantly, the model consistently produces visually enhanced results for training, improving its performance while preserving the characteristics of original biomedical images. These findings indicate a potential way to train a deep neural network in a self-revolution manner independent of real-world human data.

翻译：基于深度学习的超分辨模型有望通过有效应对早期检测、个性化医疗和临床自动化中的多重挑战，彻底改变生物医学成像与诊断领域。然而，对大量高分辨率图像的需求限制了其在临床实践中的广泛应用。本实验中，我们提出一种仅利用单张真实图像即可有效训练基于深度学习的超分辨模型的方法，通过利用自生成的高分辨率图像实现这一目标。我们采用混合图像筛选指标自动选择与真实分布相似的图像，逐步构建训练数据集，促使模型随时间生成更优图像。经过五次训练迭代后，所提出的基于深度学习的超分辨模型在结构相似度和峰值信噪比上分别提升7.5%和5.49%。值得注意的是，该模型在训练过程中持续生成视觉增强结果，在保持原始生物医学图像特征的同时优化性能。这些发现表明了一种无需依赖真实人类数据、以自演进方式训练深度神经网络的潜在途径。