  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.
