High-Resolution Hierarchical Adversarial Learning for OCT Speckle Noise Reduction

Raw optical coherence tomography (OCT) images typically are of low quality because speckle noise blurs retinal structures, severely compromising visual quality and degrading performances of subsequent image analysis tasks. In this paper, we propose a novel end-to-end cross-domain denoising framework for speckle noise suppression. We utilize high quality ground truth datasets produced by several commercial OCT scanners for training, and apply the trained model to datasets collected by our in-house OCT scanner for denoising. Our model uses the high-resolution network (HRNet) as backbone, which maintains high-resolution representations during the entire learning process to restore high fidelity images. In addition, we develop a hierarchical adversarial learning strategy for domain adaption to align distribution shift among datasets collected by different scanners. Experimental results show that the proposed model outperformed all the competing state-of-the-art methods. As compared to the best of our previous method, the proposed model improved the signal to noise ratio (SNR) metric by a huge margin of \(18.13\,\mathrm dB\) and only required \(25\,\mathrm ms\) for denoising one image in testing phase, achieving the real-time processing capability for the in-house OCT scanner.