Robust wavefront correction using AdamW-enhanced SPGD for computational adaptive optics in optical coherence tomography

Computational adaptive optics (CAO) has emerged as an effective approach to correct optical aberrations in optical coherence tomography (OCT) without the need for additional hardware. However, conventional stochastic parallel gradient descent (SPGD) algorithms often suffer from slow convergence, instability, and susceptibility to local minima. In this work, we present an AdamW-enhanced SPGD algorithm that integrates adaptive learning rate control with weight decay to accelerate convergence and stabilize optimization. In addition, an eigenvalue-based sharpness metric is introduced to ensure more robust and stable optimization. The proposed approach was validated using both simulations and two distinct swept-source OCT setups, demonstrating its applicability in realistic imaging conditions. Furthermore, our results enabled cellular-level visualization of the photoreceptor layer and retinal nerve fiber layer in retinal imaging. These findings indicate that the proposed method shows a significant advancement over conventional CAO approaches, expanding the potential for clinical translation of CAO across a variety of ophthalmic applications.