MLGF-GAN: a multi-level local-global feature fusion GAN for OCT image super-resolution

Optical coherence tomography (OCT), a non-invasive imaging modality, holds significant clinical value in cardiology and ophthalmology. However, its imaging quality is often constrained by inherently limited resolution, thereby affecting diagnostic utility. For OCT-based diagnosis, enhancing perceptual quality that emphasizes human visual recognition ability and diagnostic effectiveness is crucial. Existing super-resolution methods prioritize reconstruction accuracy (e.g., PSNR optimization) but neglect perceptual quality. To address this, we propose a Multi-level Local-Global feature Fusion Generative Adversarial Network (MLGF-GAN) that systematically integrates local details, global contextual information, and multilevel features to fully exploit the recoverable information in the image. The Local Feature Extractor (LFE) employs Coordinate Attention-enhanced convolutional neural network (CNN) for lesion-focused local feature refinement, and the Global Feature Extractor (GFE) employs shifted-window Transformers to model long-range dependencies. The Multi-level Feature Fusion Structure (MFFS) hierarchically aggregates image features and adaptively processes information at different scales. The multi-scale (×2, ×4, ×8) evaluations conducted on coronary and retinal OCT datasets demonstrate that the proposed model achieves highly competitive perceptual quality across all scales while maintaining reconstruction accuracy. The generated OCT super-resolution images exhibit superior texture detail restoration and spectral consistency, contributing to improved accuracy and reliability in clinical assessment. Furthermore, cross-pathology experiments further demonstrate that the proposed model possesses excellent generalization capability.