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

ABSTRACT
This paper presents an innovative super-resolution (SR) method for Optical Coherence Tomography (OCT),
enhancing image resolution and reducing noise without retraining for di!erent 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, o!ering a robust solution to improve OCT image
quality and medical imaging analysis.
Keywords: Super-resolution, Optical Coherence Tomography, Deep Learning, Mixture of Experts