Abstract
Medical images captured in less-than-optimal conditions may suffer from quality degradation, such as blur, artifacts, and low lighting, which potentially leads to misdiagnosis. Unfortunately, state-of-the-art medical image enhancement methods face challenges in both high-resolution image quality enhancement and local distinct anatomical structure preservation. To address these issues, we propose a Clinical-oriented High-resolution Lightweight Medical Image Enhancement Network, called CHLNet, which proficiently addresses high-resolution medical image enhancement, detailed pathological characteristics, and lightweight network design simultaneously. More specifically, CHLNet comprises two main components: 1) High-resolution Assisted Quality Enhancement Network for removing global low-quality factors in high-resolution images thus enhancing overall image quality; 2) High-quality-semantic Guided Quality Enhancement Network for capturing semantic knowledge from high-quality images such that detailed structure preservation is enforced. Moreover, thanks to its lightweight design, CHLNet can be easily deployed on medical edge devices. Extensive experiments on three public medical image datasets demonstrate the effectiveness and superiority of CHLNet over the state-of-the-art.
Y. Wang, L. Chen and Q. Hou—Contribute equally to this work.
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Acknowledgments
This research was supported by the National Natural Science Foundation of China (No. 62076059), the Science and Technology Joint Project of Liaoning province (2023JH2/101700367), and the Fundamental Research Funds for the Central Universities (No. N2424010-7).
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Wang, Y. et al. (2024). A Clinical-Oriented Lightweight Network for High-Resolution Medical Image Enhancement. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15003. Springer, Cham. https://doi.org/10.1007/978-3-031-72384-1_1
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