Speckle Noise Reduction in OCT Retinal Images Based on a Multi-Scale Self-Attention Generative Adversarial Network

Optical coherence tomography (OCT) is widely used in biomedical imaging and ophthalmology. However, OCT images are frequently corrupted by speckle noise from coherent light interference. This degradation hampers clinical diagnosis of retinal lesions and identification of tissue layers. We propose a speckle noise reduction algorithm based on a Multi-Scale Self-Attention Generative Adversarial Network combined with a Siamese network (SA-Siamese-GAN). To address the limited receptive fields of traditional convolutional neural networks (CNNs), which can cause broken or blurred retinal layers, a self-attention mechanism is integrated into the bottleneck layer of the generator to capture global pixel dependencies. A Siamese network enforces structural consistency, and a joint loss function combining Wasserstein distance, perceptual loss, and structural similarity (SSIM) is used. Experiments on 11,103 pairs of clinical OCT retinal images show that SA-Siamese-GAN outperforms BM3D, NLM, Wavelet, and GAN-ResNet. It removes speckle noise while preserving retinal layer structure and fine textures, and achieves the highest Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM).