  Optical coherence tomography (OCT) is a noninvasive technology that enables
real-time imaging of tissue microanatomies. The axial resolution of OCT is
intrinsically constrained by the spectral bandwidth of the employed light
source while maintaining a fixed center wavelength for a specific application.
Physically extending this bandwidth faces strong limitations and requires a
substantial cost. We present a novel computational approach, called as O-PRESS,
for boosting the axial resolution of OCT with Prior Guidance, a Recurrent
mechanism, and Equivariant Self-Supervision. Diverging from conventional
superresolution methods that rely on physical models or data-driven techniques,
our method seamlessly integrates OCT modeling and deep learning, enabling us to
achieve real-time axial-resolution enhancement exclusively from measurements
without a need for paired images. Our approach solves two primary tasks of
resolution enhancement and noise reduction with one treatment. Both tasks are
executed in a self-supervised manner, with equivariance imaging and free space
priors guiding their respective processes. Experimental evaluations,
encompassing both quantitative metrics and visual assessments, consistently
verify the efficacy and superiority of our approach, which exhibits performance
on par with fully supervised methods. Importantly, the robustness of our model
is affirmed, showcasing its dual capability to enhance axial resolution while
concurrently improving the signal-to-noise ratio.
