A GAN-based deep enhancer for quality enhancement of retinal images photographed by a handheld fundus camera

Fu, Junxia; Cao, Lvchen; Wei, Shimin; Xu, Min; Song, Yali; Li, Huiqi; You, Yuxia

doi:10.1016/j.aopr.2022.100077

Cited by 3 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With this approach, the signal-to-noise ratio of scans with scattering noise accompanying artifacts was significantly improved. In addition, Fu et al [17] proposed an automated approach using a combination of two mirror-symmetric generative adversarial networks for image enhancement tasks. This approach demonstrated broad applicability for different tasks.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

“…In recent years, research for symptomatic vitreous opacity image enhancement has made significant progress in several areas. These studies typically use traditional methods [11][12][13][14][15] and deep learning methods [16][17][18][19][20] to improve the quality and clarity of symptomatic vitreous opacity images. Some studies use traditional methods, such as filtering, to improve the quality of symptomatic vitreous opacity images.…”

Section: Related Workmentioning

confidence: 99%

“…Although conventional methods perform well in some aspects, they may not be able to fully utilize the advantages of advanced techniques such as deep learning. In the case of deep learning methods [16][17][18][19][20], despite significant progress in image enhancement, a large amount of labeled data is usually required to train the model. Especially for symptomatic vitreous opacity image enhancement, obtaining a sufficient amount of labeling data can be a challenging task.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Symptomatic vitreous opacity image enhancement using linear and nonlinear transformations

Zhang,

Ye,

Rao

2024

Preprint

View full text Add to dashboard Cite

The rapid development of digital ophthalmology has put forward new demands for symptomatic vitreous opacity image processing, and symptomatic vitreous opacity image enhancement has become a key issue in the research of digital ophthalmology development. Focusing on this key issue, this paper proposes a linear and nonlinear transform-based image enhancement for symptomatic vitreous opacity. Specifically, firstly, a sharpening and white balance correction module is proposed, and the symptomatic vitreous opacity image is sharpened to compensate for the loss of details in the exposed area. Secondly, a visibility restoration module based on type II fuzzy sets is designed. We propose a symptomatic vitreous opacity image enhancement method with interpretability. As a result, the proposed method can solve the exposure and low visibility problems occurring in the symptomatic vitreous opacity image, and produce enhancement results that conform to human visual characteristics and have interpretability. Through comparative experimental analysis, the proposed method achieves superior results compared to advanced image enhancement methods.

show abstract

Section: Deep Learning Methodsmentioning

confidence: 99%