Dehazing Algorithm for Enhancing Fundus Photographs Using Dark Channel and Bright Channel Prior

Sehie Park, Hyungjin Chung, Jong Chul Ye, Kayoung Yi
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Abstract

Purpose: We present a dehazing algorithm using dark channel prior (DCP) and bright channel prior (BCP) to enhance the quality of retinal images obtained through conventional fundus photography.Methods: A retrospective analysis was conducted on retinal images from patients who visited Gangnam Sacred Heart Hospital between January 2000 and September 2022. These images were captured using a digital fundus camera (KOWA Nonmyd 8S Fundus Camera, KOWA Company, Nagoya, Japan) without pupil dilation. We used two mathematical algorithms: DCP only and DCP and BCP combined. The original, DCP-processed, and DCP & BCP-processed images were compared. Fisher's exact test was used to identify significant quality improvements.Results: The DCP and the newly proposed DCP plus BCP algorithm effectively eliminated haze and enhanced the contrast of cataract images. Notably, DCP demonstrated limited improvements in fundus photographs from patients with small pupils, whereas the proposed DCP plus BCP method effectively revealed previously obscured retinal details and vessels. However, these methods exhibited limited performance in severe cataracts compared to the clear images obtained after surgery. The quality enhancement with the proposed method was significant in photographs of patients with cataracts (p = 0.032) and small pupils (p < 0.01).Conclusions: Our algorithm produced clearer images of blood vessels and optic disc structures, while significantly reducing artifacts in fundus images from patients with small pupils or cataracts. The proposed algorithm can provide visually enhanced images, potentially aiding physicians in the diagnosis of retinal diseases in patients with cataracts.
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使用暗通道和亮通道先验值增强眼底照片的去渐变算法
目的:我们提出了一种使用暗通道先验(DCP)和亮通道先验(BCP)的去毛刺算法,以提高通过传统眼底摄影获得的视网膜图像的质量:对 2000 年 1 月至 2022 年 9 月期间在江南圣心医院就诊的患者视网膜图像进行了回顾性分析。这些图像是在不散瞳的情况下使用数字眼底照相机(KOWA Nonmyd 8S 眼底照相机,KOWA 公司,日本名古屋)拍摄的。我们使用了两种数学算法:仅 DCP 算法和 DCP 与 BCP 结合算法。我们比较了原始图像、DCP 处理图像以及 DCP 和 BCP 处理图像。使用费雪精确检验来确定显著的质量改进:结果:DCP 和新提出的 DCP 加 BCP 算法有效地消除了白内障图像的雾度并增强了对比度。值得注意的是,DCP 在小瞳孔患者的眼底照片中表现出有限的改善,而提议的 DCP 加 BCP 方法则有效地显示了之前被遮挡的视网膜细节和血管。然而,与手术后获得的清晰图像相比,这些方法在严重白内障中的表现有限。在白内障(p = 0.032)和小瞳孔(p < 0.01)患者的照片中,拟议方法的质量提升效果显著:我们的算法能生成更清晰的血管和视盘结构图像,同时显著减少小瞳孔或白内障患者眼底图像中的伪影。所提出的算法可提供视觉增强图像,为医生诊断白内障患者的视网膜疾病提供潜在帮助。
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126
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