End-to-end residual attention mechanism for cataractous retinal image dehazing. (June 2022)

• Record Type: Journal Article
• Authors: Qiu, Defu; Cheng, Yuhu; Wang, Xuesong
• Is Part Of: Computer methods and programs in biomedicine. Volume 219(2022)
• Publication Date: 2022-06
• Subjects: Cataractous -- Dehaze -- Retinal image -- Skip connection -- Residual attention mechanism; 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.2022.106779
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Developed an end-to-end residual attention mechanism framework. Designed the multi-scale feature extraction module. Based on the encoding-decoding structure. Achieved good subjective and objective evaluation results, and the effect of retinal dehazing is better. Abstract: Background and objective: Cataract is one of the most common causes of vision loss. Light scattering due to clouding of the lens in cataract patients makes it extremely difficult to image the retina of cataract patients with fundus cameras, resulting in a serious decrease in the quality of the retinal images taken. Furthermore, the age of cataract patients is generally too old, in addition to cataracts, the patients often have other retinal diseases, which brings great challenges to experts in the clinical diagnosis of cataract patients using retinal imaging. Methods: In this paper, we present the End-to-End Residual Attention Mechanism (ERAN) for Cataractous Retinal Image Dehazing, which it includes four modules: encoding module, multi-scale feature extraction module, feature fusion module, and decoding module. The encoding module encodes the input cataract haze image into an image, facilitating subsequent feature extraction and reducing memory usage. The multi-scale feature extraction module includes a hole convolution module, a residual block, and an adaptive skip connection, which can expand the receptive field and extract features of different scales through weighted screening for fusion. The feature fusion module uses adaptive skip connections to enhance the network's ability to extract haze density images to make haze removal more thorough. Furthermore, the decoding module performs non-linear mapping on the fused features to obtain the haze density image, and then restores the haze-free image. Results: The experimental results show that the proposed method has achieved better objective and subjective evaluation results, and has a better dehazing effect. Conclusion: We proposed ERAN method not only provides visually better images, but also helps experts better diagnose other retinal diseases in cataract patients, leading to better care and treatment.