Improved generative adversarial network for retinal image super-resolution. (October 2022)

• Record Type: Journal Article
• Authors: Qiu, Defu; Cheng, Yuhu; Wang, Xuesong
• Is Part Of: Computer methods and programs in biomedicine. Volume 225(2022)
• Publication Date: 2022-10
• Subjects: Super-resolution -- Retinal image -- Generative adversarial network -- Residual learning -- Convolutional neural network; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106995
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Developed an improved generative adversarial network. Designed a novel residual attention block. Use the Charbonnier loss function instead of the MSE loss function. Remove the BN layer and add multiple updated residual blocks. Abstract: Background and objective: 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. Methods: 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. 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. Conclusion: 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.