Table 4.
Summary of literature review for image enhancement (denoising and super-resolution) tasks using GAN in ophthalmology imaging domains
| Publication | Basic technique | Domain | Target | Summary |
|---|---|---|---|---|
| Halupka et al. [48] | Modified Wasserstein GAN + perceptual loss (conditional GAN) | Retinal OCT (spectral domain) | Removing speckle noise | The GAN was used to reduce speckle artifacts in retinal OCT images. The method improved the image quality metrics for OCT |
| Mahapatra et al. [55] | PGGAN with a conditional design | Fundus photography | Super-resolution | Image super-resolution using multi-stage PGGAN outperforms competing methods and baseline GANs. The super-resolved images can be used for landmark and pathology detection |
| Huang et al. [49] | Conditional GAN | Retinal OCT | Super-resolution and removing noise | The GAN model effectively suppressed speckle noise and super-resolved OCT images at different scales |
| Ouyang et al. [51] | Conditional GAN | Anterior Segment OCT | Removing speckle noise | The model removed undesired specular artifacts and speckle-noise patterns to improve the visualization of corneal and limbal OCT images |
| Yoo et al. [53] | CycleGAN | Fundus photography | Removing artifacts and noise | The GAN model removed the artifacts automatically in a fundus photograph without matching paired images |
| Cheong et al. [16] | DeshadowGAN (modified conditional GAN with perceptual loss) | Peripapillary retinal OCT (spectral domain) | Removing vessel shadow artifacts | The GAN model using manually masked artifact images and perceptual loss function removed blood vessel shadow artifacts from OCT images of the optic nerve head |
| Chen et al. [50] | Conditional GAN | Peripapillary retinal OCT (spectral domain) | Removing speckle noise | The GAN model was designed for speckle noise reduction in OCT images and preserved the textural details found in OCT |
| Das et al. [52] | CycleGAN | Retinal OCT | Super-resolution and removing noise | To achieve denoising and super-resolution, adversarial learning with cycle consistency was used without requiring aligned low–high resolution pairs |
| Ha et al. [56] | Enhanced super-resolution GAN (SRGAN) | Peripapillary fundus photography (optic disc photo) | Super-resolution | The GAN approach was capable of 4-times up-scaling and enhancement of anatomical details using contrast, color, and brightness improvement |
| Yuhao et al. [54] | CycleGAN | Fundus photography | Removing artifacts and noise | The developed model dehazed cataractous retinal images through unpaired clear retinal images and cataract images |
GAN = generative adversarial network; OCT = optical coherence tomography; PGGAN = progressively growing GAN