Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China; School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
Comput Methods Programs Biomed. 2022 Oct;225:106995. doi: 10.1016/j.cmpb.2022.106995. Epub 2022 Jul 1.
The retina is the only organ in the body that can use visible light for non-invasive observation. By analyzing retinal images, we can achieve early screening, diagnosis and prevention of many ophthalmological and systemic diseases, helping patients avoid the risk of blindness. Due to the powerful feature extraction capabilities, many deep learning super-resolution reconstruction networks have been applied to retinal image analysis and achieved excellent results.
Given the lack of high-frequency information and poor visual perception in the current reconstruction results of super-resolution reconstruction networks under large-scale factors, we present an improved generative adversarial network (IGAN) algorithm for retinal image super-resolution reconstruction. Firstly, we construct a novel residual attention block, improving the reconstruction results lacking high-frequency information and texture details under large-scale factors. Secondly, we remove the Batch Normalization layer that affects the quality of image generation in the residual network. Finally, we use the more robust Charbonnier loss function instead of the mean square error loss function and the TV regular term to smooth the training results.
Experimental results show that our proposed method significantly improves objective evaluation indicators such as peak signal-to-noise ratio and structural similarity. The obtained image has rich texture details and a better visual experience than the state-of-the-art image super-resolution methods.
Our proposed method can better learn the mapping relationship between low-resolution and high-resolution retinal images. This method can be effectively and stably applied to the analysis of retinal images, providing an effective basis for early clinical treatment.
视网膜是人体唯一可利用可见光进行非侵入性观察的器官。通过分析视网膜图像，我们可以实现对许多眼科和系统性疾病的早期筛查、诊断和预防，帮助患者避免失明的风险。由于强大的特征提取能力，许多深度学习超分辨率重建网络已应用于视网膜图像分析，并取得了优异的结果。
鉴于当前超分辨率重建网络在大尺度因子下的重建结果缺乏高频信息和较差的视觉感知，我们提出了一种用于视网膜图像超分辨率重建的改进生成对抗网络（IGAN）算法。首先，我们构建了一个新颖的残差注意力块，改善了大尺度因子下缺乏高频信息和纹理细节的重建结果。其次，我们移除了残差网络中影响图像生成质量的批量归一化层。最后，我们使用更稳健的 Charbonnier 损失函数代替均方误差损失函数和 TV 正则项来平滑训练结果。
实验结果表明，我们提出的方法显著提高了峰值信噪比和结构相似性等客观评估指标。得到的图像具有丰富的纹理细节和更好的视觉体验，优于最先进的图像超分辨率方法。
我们提出的方法可以更好地学习低分辨率和高分辨率视网膜图像之间的映射关系。该方法可以有效地、稳定地应用于视网膜图像的分析，为早期临床治疗提供有效的依据。
