Edge-Preserving Denoising and Super-Resolution in OCT Imagery Using Deep SMoE Gating Networks

This paper presents an innovative super-resolution (SR) method for Optical Coherence Tomography (OCT), enhancing image resolution and reducing noise without retraining for different scales. Traditional SR techniques, interpolation, reconstruction, and learning-based, are surpassed by our approach, which combines a "shifted steered mixture of experts" with an autoencoder. This method outperforms the latest algorithms in subjective and objective evaluations, including PSNR and perceptual metrics. A distinctive feature is the adjustable sharpness, enabling targeted edge sharpening or defocusing through kernel experts' bandwidth adjustments. This adaptability negates the need for data-specific retraining, offering a robust solution to improve OCT image quality and medical imaging analysis.