Iterative-in-iterative super-resolution biomedical imaging using one real image

Yuanzheng Ma; Xinyue Wang; Benqi Zhao; Ying Xiao; Shijie Deng; Jian Song; Xun Guan

doi:10.1117/12.2691281

28 July 2023 Iterative-in-iterative super-resolution biomedical imaging using one real image

Yuanzheng Ma, Xinyue Wang, Benqi Zhao, Ying Xiao, Shijie Deng, Jian Song, Xun Guan

Proceedings Volume 12753, Second Conference on Biomedical Photonics and Cross-Fusion (BPC 2023); 1275308 (2023) https://doi.org/10.1117/12.2691281
Event: Second Conference on Biomedical Photonics and Cross-Fusion (BPC 2023), 2023, Shanghai, China

Abstract

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.

Citation Download Citation

Yuanzheng Ma, Xinyue Wang, Benqi Zhao, Ying Xiao, Shijie Deng, Jian Song, and Xun Guan "Iterative-in-iterative super-resolution biomedical imaging using one real image", Proc. SPIE 12753, Second Conference on Biomedical Photonics and Cross-Fusion (BPC 2023), 1275308 (28 July 2023); https://doi.org/10.1117/12.2691281

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KEYWORDS

Education and training

Super resolution

Biomedical optics

Image processing

Data modeling

Image restoration

Image quality

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