ArticleBiomedical optics express2024
Semi-supervised generative adversarial learning for denoising adaptive optics retinal images.
Article in Biomedical optics express, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study presents denoiseGAN, a novel semi-supervised generative adversarial network, for denoising adaptive optics (AO) retinal images. By leveraging both synthetic and real-world data, denoiseGAN effectively addresses various noise sources, including blur, motion artifacts, and electronic noise, commonly found in AO retinal imaging. Experimental results demonstrate that denoiseGAN outperforms traditional image denoising methods and the state-of-the-art conditional GAN model, preserving retinal cell structures and enhancing image contrast. Moreover, denoiseGAN aids downstream analysis, improving cell segmentation accuracy. Its 30% faster computational efficiency makes it a potential choice for real-time AO image processing in ophthalmology research and clinical practice.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.