使用自适应图总变化的视网膜OCT图像的盲去模糊。

Applied optics Pub Date : 2025-09-20 DOI:10.1364/AO.571832
Jiamin Wang, Shujun Men, Yang Tao, Yanke Li, Lei Zhang, Li Huo
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引用次数: 0

摘要

我们提出了一种盲去模糊方法,用于视网膜光学相干断层扫描(OCT)图像的深度相关空间变模糊退化。我们的方法利用自适应图总变化(AGTV)先验,它使用输入图像的局部梯度统计动态调整正则化权重。AGTV自动增强了严重模糊的深层区域的平滑,同时保留了浅层的精细结构。经过微球图像、正面图像和b扫描的验证,AGTV在PSNR/SSIM指标上优于最先进的方法,并显着提高了视网膜层分割的准确性,特别是对于深边界。这种单图像框架不需要预定义的PSF模型或硬件修改,为临床OCT增强提供了潜在的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Blind deblurring of retinal OCT images using an adaptive graph total variation.

We propose a blind deblurring method for retinal optical coherence tomography (OCT) images degraded by depth-dependent spatially variant blur. Our approach leverages an adaptive graph total variation (AGTV) prior, which dynamically adjusts regularization weights using local gradient statistics from the input image. AGTV autonomously enhances smoothing in severely blurred deep regions while preserving fine structures in shallow layers. Validated on microsphere images, en-face images, and B-scans, AGTV outperforms state-of-the-art methods in PSNR/SSIM metrics and significantly improves retinal layer segmentation accuracy-particularly for deep boundaries. This single-image framework requires no predefined PSF models or hardware modifications, offering a potential solution for clinical OCT enhancement.

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