Abdusalomov Akmalbek, Mirzakhalilov Sanjar, Dilnoza Zaripova, Zohirov Kudratjon, Nasimov Rashid, Umirzakova Sabina, Cho Young-Im
Department of Computer Engineering, Gachon University Sujeong-Gu, Seongnam-Si 13120, Gyeonggi-Do, Republic of Korea.
Department of Computer Systems/Information and Educational Technologies, Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi, Tashkent 100200, Uzbekistan.
Bioengineering (Basel). 2024 Nov 21;11(12):1179. doi: 10.3390/bioengineering11121179.
Medical imaging plays an essential role in modern healthcare, providing non-invasive tools for diagnosing and monitoring various medical conditions. However, the resolution limitations of imaging hardware often result in suboptimal images, which can hinder the precision of clinical decision-making. Single image super-resolution (SISR) techniques offer a solution by reconstructing high-resolution (HR) images from low-resolution (LR) counterparts, enhancing the visual quality of medical images. In this paper, we propose an enhanced Residual Feature Learning Network (RFLN) tailored specifically for medical imaging. Our contributions include replacing the residual local feature blocks with standard residual blocks, increasing the model depth for improved feature extraction, and incorporating enhanced spatial attention (ESA) mechanisms to refine the feature selection. Extensive experiments on medical imaging datasets demonstrate that the proposed model achieves superior performance in terms of both quantitative metrics, such as PSNR and SSIM, and qualitative visual quality compared to existing state-of-the-art models. The enhanced RFLN not only effectively mitigates noise but also preserves critical anatomical details, making it a promising solution for high-precision medical imaging applications.
医学成像在现代医疗保健中发挥着至关重要的作用，为诊断和监测各种医疗状况提供了非侵入性工具。然而，成像硬件的分辨率限制常常导致图像质量欠佳，这可能会妨碍临床决策的精确性。单图像超分辨率（SISR）技术通过从低分辨率（LR）图像重建高分辨率（HR）图像，提高医学图像的视觉质量，从而提供了一种解决方案。在本文中，我们提出了一种专门为医学成像量身定制的增强型残差特征学习网络（RFLN）。我们的贡献包括用标准残差块替换残差局部特征块、增加模型深度以改进特征提取，以及纳入增强空间注意力（ESA）机制以优化特征选择。在医学成像数据集上进行的大量实验表明，与现有的最先进模型相比，所提出的模型在诸如PSNR和SSIM等定量指标以及定性视觉质量方面均取得了卓越的性能。增强型RFLN不仅有效地减轻了噪声，还保留了关键的解剖细节，使其成为高精度医学成像应用的一个有前景的解决方案。
