Noise-Powered Disentangled Representation for Unsupervised Speckle Reduction of Optical Coherence Tomography Images

Due to its noninvasive character, optical coherence tomography (OCT) has become a popular diagnostic method in clinical settings. However, the low-coherence interferometric imaging procedure is inevitably contaminated by heavy speckle noise, which impairs both visual quality and diagnosis of various...

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Veröffentlicht in:	IEEE transactions on medical imaging 2021-10, Vol.40 (10), p.2600-2614
Hauptverfasser:	Huang, Yongqiang, Xia, Wenjun, Lu, Zexin, Liu, Yan, Chen, Hu, Zhou, Jiliu, Fang, Leyuan, Zhang, Yi
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Coders Deep learning Disentangled representation Eye diseases Feature extraction Generative adversarial networks Generators Image denoising Image reconstruction Machine learning Medical imaging Noise Noise measurement Noise pollution Noise reduction Optical Coherence Tomography Representations Speckle speckle reduction Tomography unsupervised learning
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