Untrained Network Prior with Spectral Bias Compensation for Speckle Removal in AS-OCT

Speckle removal in anterior segment optical coherence tomography (AS-OCT) images is a nonlinear inverse problem that improves image quality. Although untrained network priors have proven effective in inverse restoration tasks but are not directly applicable to speckle removal in clinical AS-OCT images due to spectral bias: networks focus more on low-frequency information, whereas high-frequency structural details are vital for clinical analysis. In this paper, we aim to boost the Untrained network Priors for AS-OCT image despeckling by Spectral Bias Compensation (UP-SBC), which enriches the structural information and overall regularization. Specifically, we compensate for the loss of high-frequency structural information and analyze its efficacy via analyzing the frequency band correspondence. Then, we further design a data fidelity to regularize the nonlinear nature of speckle removal and incorporate a focal frequency loss to decouple the structural details and speckle noise for network optimization. Experiments verify the efficacy of the UP-SBC, providing high-quality despeckling results while preserving structural details.