In the field of medical image analysis, there is a substantial need for high-resolution (HR) images to improve diagnostic accuracy. However, It is a challenging task to obtain HR medical images, as it requires advanced instruments and significant time. Deep learning-based super-resolution methods can help to improve the resolution and perceptual quality of low-resolution (LR) medical images. Recently, Generative Adversarial Network (GAN) based methods have shown remarkable performance among deep learning-based super-resolution methods. Real-Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN) is a practical model for recovering HR images from real-world LR images. In our proposed approach, we use transfer learning technique and fine-tune the pre-trained Real-ESRGAN model using medical image datasets. This technique helps in improving the performance of the model. The focus of this paper is on enhancing the resolution and perceptual quality of chest X-ray and retinal images. We use the Tuberculosis chest X-ray (Shenzhen) dataset and the STARE dataset of retinal images for fine-tuning the model. The proposed model achieves superior perceptual quality compared to the Real-ESRGAN model, effectively preserving fine details and generating images with more realistic textures.

翻译：在医学图像分析领域，为提高诊断准确性，对高分辨率（HR）图像有着迫切需求。然而，获取高分辨率医学图像是一项具有挑战性的任务，因为需要先进的仪器和大量的时间。基于深度学习的超分辨率方法有助于提高低分辨率（LR）医学图像的分辨率和感知质量。近年来，基于生成对抗网络（GAN）的方法在深度学习超分辨率方法中展现出卓越的性能。真实增强超分辨率生成对抗网络（Real-ESRGAN）是一种从真实世界低分辨率图像恢复高分辨率图像的实用模型。在我们提出的方法中，采用迁移学习技术，利用医学图像数据集对预训练的Real-ESRGAN模型进行微调。该技术有助于提升模型性能。本文重点在于提高胸部X光图像和视网膜图像的分辨率与感知质量。我们使用结核病胸部X光（深圳）数据集和STARE视网膜图像数据集对模型进行微调。与Real-ESRGAN模型相比，所提出的模型实现了更优的感知质量，能够有效保留精细细节并生成具有更真实纹理的图像。