Anatomy-guided weakly supervised learning framework for corneal nerve image denoising and enhancement

Corneal confocal microscopy (CCM) enables non-invasive imaging of the sub-basal nerve plexus for early diagnosis of diabetic neuropathy, but its utility is hindered by inherent noise and low contrast in raw images. We present NerveBoost, a weakly supervised framework for CCM denoising and enhancement guided by anatomical priors. Unlike supervised methods requiring paired clean data, NerveBoost uses binary nerve masks to construct a pseudo-target via region-specific gamma correction. A composite loss function integrates weighted reconstruction, gradient consistency, background smoothness, and foreground-background contrast constraints to jointly optimize noise suppression and structural enhancement within an encoder-decoder architecture. Results indicate that NerveBoost effectively enhances nerve visibility while maintaining structural fidelity, offering a robust and efficient preprocessing solution for clinical CCM analysis without requiring paired ground-truth data.