Artifact Reduction in Fundus Imaging using Cycle Consistent Adversarial Neural Networks
Fundus images are very useful in identifying various ophthalmic disorders. However, due to the presence of artifacts, the visibility of the retina is severely affected. This may result in misdiagnosis of the disorder which may lead to more complicated problems. Since deep learning is a powerful tool to extract patterns from data without much human intervention, they can be applied to image-to-image translation problems. An attempt has been made in this paper to automatically rectify such artifacts present in the images of the fundus. We use a CycleGAN based model which consists of residual blocks to reduce the artifacts in the images. Significant improvements are seen when compared to the existing techniques.
Code (0)
등록된 구현이 없습니다.
Tasks
Image-to-Image TranslationTranslationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Unsupervised Metal Artifact Reduction in Dental CBCT using Fine-tuned Cycle-Consistent Adversarial Networks
Metal artifacts generated by dental implants significantly degrade cone-beam computed tomography (CBCT) volumes, obscuring critical anatomical structures and compromising diagnostic precision. To address this, an unsuper…
Progressive Transfer Learning for Multi-Pass Fundus Image Restoration
Diabetic retinopathy is a leading cause of vision impairment, making its early diagnosis through fundus imaging critical for effective treatment planning. However, the presence of poor quality fundus images caused by fac…
Diabetic Retinopathy DetectionImage RestorationTransfer LearningDiffusion Autoencoder for Unsupervised Artifact Restoration in Handheld Fundus Images
The advent of handheld fundus imaging devices has made ophthalmologic diagnosis and disease screening more accessible, efficient, and cost-effective. However, images captured from these setups often suffer from artifacts…
Image RestorationA portable widefield fundus camera with high dynamic range imaging capability
Fundus photography is indispensable for clinical detection and management of eye diseases. Limited image contrast and field of view (FOV) are common limitations of conventional fundus cameras, making it difficult to dete…
ManagementVocal Bursts Intensity PredictionModeling and Enhancing Low-quality Retinal Fundus Images
Retinal fundus images are widely used for the clinical screening and diagnosis of eye diseases. However, fundus images captured by operators with various levels of experience have a large variation in quality. Low-qualit…
Image EnhancementMedical Image AnalysisRetinal Vessel Segmentation