Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin, China.
Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin, China.
Comput Biol Med. 2024 Jan;168:107708. doi: 10.1016/j.compbiomed.2023.107708. Epub 2023 Nov 19.
Retinal fundus imaging is a crucial diagnostic tool in ophthalmology, enabling the early detection and monitoring of various ocular diseases. However, capturing high-resolution fundus images often presents challenges due to factors such as defocusing and diffraction in the digital imaging process, limited shutter speed, sensor unit density, and random noise in the image sensor or during image transmission. Super-resolution techniques offer a promising solution to overcome these limitations and enhance the visual details in retinal fundus images. Since the retina has rich texture details, the super-resolution images often introduce artifacts into texture details and lose some fine retinal vessel structures. To improve the perceptual quality of the retinal fundus image, a generative adversarial network that consists of a generator and a discriminator is proposed. The proposed generator mainly comprises 23 multi-scale feature extraction blocks, an image segmentation network, and 23 residual-in-residual dense blocks. These components are employed to extract features at different scales, acquire the retinal vessel grayscale image, and extract retinal vascular features, respectively. The generator has two branches that are mainly responsible for extracting global features and vascular features, respectively. The extracted features from the two branches are fused to better restore the super-resolution image. The proposed generator can restore more details and more accurate fine vessel structures in retinal images. The improved discriminator is proposed by introducing our designed attention modules to help the generator yield clearer super-resolution images. Additionally, an artifact loss function is also introduced to enhance the generative adversarial network, enabling more accurate measurement of the disparity between the high-resolution image and the restored image. Experimental results show that the generated images obtained by our proposed method have a better perceptual quality than the state-of-the-art image super-resolution methods.
眼底图像是眼科诊断的重要工具，可以早期发现和监测各种眼部疾病。然而，由于数字成像过程中的散焦和衍射、有限的快门速度、传感器单元密度以及图像传感器或图像传输过程中的随机噪声等因素，获取高分辨率眼底图像通常具有挑战性。超分辨率技术为克服这些限制和增强眼底图像的视觉细节提供了一种有前途的解决方案。由于视网膜具有丰富的纹理细节，因此超分辨率图像通常会在纹理细节中引入伪影，并丢失一些精细的视网膜血管结构。为了提高眼底图像的感知质量，提出了一种由生成器和鉴别器组成的生成对抗网络。所提出的生成器主要由 23 个多尺度特征提取块、图像分割网络和 23 个残差密集块组成。这些组件用于在不同尺度上提取特征、获取视网膜血管灰度图像和提取视网膜血管特征。生成器有两个分支，主要负责分别提取全局特征和血管特征。从两个分支提取的特征融合在一起，以更好地恢复超分辨率图像。所提出的生成器可以在视网膜图像中恢复更多的细节和更准确的精细血管结构。所提出的改进鉴别器通过引入我们设计的注意模块来帮助生成器生成更清晰的超分辨率图像。此外，还引入了伪像损失函数来增强生成对抗网络，从而更准确地测量高分辨率图像和恢复图像之间的差异。实验结果表明，与最先进的图像超分辨率方法相比，我们提出的方法生成的图像具有更好的感知质量。
