Zero-Shot Enhancement with Cross-Modal Applicability for Low-Light Vis-μOCT Images.

Optical coherence tomography (OCT) is a rapid and non-destructive imaging technique, but image brightness decreases when imaging deep tissues or under low power and short exposure due to insufficient backscattered light. This issue is more pronounced in visible-light micro-OCT (vis-μOCT), where shorter wavelengths increase scattering and limit penetration, restricting its application. In this paper, we propose DifNIR, a novel framework for enhancing low-light OCT images. The framework begins with a preliminary denoising stage. Image enhancement is then performed using a neural implicit representation (NIR) network, in which pixel values are incorporated as auxiliary input to mitigate the oversmoothing effect of fully connected layers. To enable unsupervised learning, customdesigned loss functions is employed. The proposed method is validated through qualitative and quantitative comparisons on a self-collected en face image dataset. To further assess its generalizability, we also performed experiments on B-scan images and retinal images acquired from other OCT devices. On the en face image dataset, Dif-NIR outperforms existing methods in terms of visual quality, SNR (58.99 dB), CNR (49.56 dB), and NIQE (9.0553). It also effectively generalizes to OCT B-scan images and retinal images acquired by other devices. The proposed network effectively mitigates unpredictable brightness degradation, producing clearer and better-illuminated images while exhibiting strong generalization capability. The network effectively reveals deeplayer information in OCT images and can be applied to expand its usage scenarios to cost-effective and high-speed imaging settings.