Axial Super-Resolution in Optical Coherence Tomography Images via Spectrum-Based Self-Supervised Training

High axial resolution in Optical Coherence Tomography (OCT) images is essential for accurately diagnosing skin conditions like psoriasis and keratoderma, where clear boundary delineation can reveal early disease markers. Existing deep learning super-resolution methods typically rely on intensity-based training, which only utilizes magnitude data from the OCT spectrum after Fourier transformation, limiting the reconstruction of fine boundary details. This study introduces a spectrum-based, self-supervised deep learning framework that leverages OCT spectral (fringe) data to improve axial resolution beyond system limits. By training the model directly on fringe data in a self-supervised manner, we achieve finer structural detail recovery. Evaluation metrics included Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and axial resolution estimation. Our framework yielded a 50% improvement in axial resolution, achieving 4.28 μm from 7.19 μm, along with PSNR gains of up to 3.37 dB and SSIM increases by 0.157, significantly enhancing boundary continuity and fine detail reconstruction. Our method surpasses intensity-based approaches in enhancing axial resolution and presents potential for iterative application to achieve even greater improvements. Significance: This framework advances OCT imaging, offering a promising, non-invasive tool for dermatological diagnostics.