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Versatile cataract fundus image restoration model utilizing unpaired cataract and high-quality images

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机构: [1]Tsinghua Univ, Tsinghua Shenzhen Int Grad Sch, Shenzhen, Peoples R China [2]Capital Med Univ, Beijing Tongren Hosp, Beijing Key Lab Intraocular Tumor Diag & Treatment, Beijing Tongren Eye Ctr,Beijing Ophthalmol & Visua, Beijing 100730, Peoples R China
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Cataract is one of the most common blinding eye diseases and can be treated by surgery. However, because cataract patients may also suffer from other blinding eye diseases, ophthalmologists must diagnose them before surgery. The cloudy lens of cataract patients forms a hazy degeneration in the fundus images, making it challenging to observe the patient's fundus vessels, which brings difficulties to the diagnosis process. To address this issue, this paper establishes a new cataract image restoration method named Catintell. It contains a cataract image synthesizing model, Catintell-Syn, and a restoration model, Catintell-Res. Catintell-Syn uses GAN architecture with fully unsupervised data to generate paired cataract-like images with realistic style and texture rather than the conventional Gaussian degradation algorithm. Meanwhile, Catintell-Res is an image restoration network that can improve the quality of real cataract fundus images using the knowledge learned from synthetic cataract images. Extensive experiments show that Catintell-Res outperforms other cataract image restoration methods in PSNR with 39.03 and SSIM with 0.9476. Furthermore, the universal restoration ability that Catintell-Res gained from unpaired cataract images can process cataract images from various datasets. We hope the models can help ophthalmologists identify other blinding eye diseases of cataract patients and inspire more medical image restoration methods in the future.

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出版当年[2025]版:
大类 | 3 区 综合性期刊
小类 | 3 区 综合性期刊
最新[2025]版:
大类 | 3 区 综合性期刊
小类 | 3 区 综合性期刊
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出版当年[2023]版:
Q1 MULTIDISCIPLINARY SCIENCES
最新[2023]版:
Q1 MULTIDISCIPLINARY SCIENCES

影响因子: 最新[2023版] 最新五年平均 出版当年[2023版] 出版当年五年平均 出版前一年[2022版]

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第一作者机构: [1]Tsinghua Univ, Tsinghua Shenzhen Int Grad Sch, Shenzhen, Peoples R China
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