Optical coherence refractive tomography (OCRT) addresses the anisotropic resolution in conventional optical coherence tomography (OCT) imaging, effectively reducing detail loss caused by resolution non-uniformity, and demonstrates strong potential across a range of biomedical applications. Full-range OCRT technique eliminates conjugate image artifacts and further extends the imaging field, enabling large-scale isotropic reconstruction. However, the isotropic resolution achieved through OCRT remains inherently limited by the maximum resolution of the acquired input data, both in the axial and lateral dimensions. Enhancing the resolution of the original images is therefore critical for achieving higher-isotropic reconstruction. Existing OCT super-resolution methods often exacerbate imaging noise during iterative processing, resulting in reconstructions dominated by noise artifacts. In this work, we present sparse continuous full-range optical coherence refractive tomography (SC-FROCRT), which integrates deconvolution-based super-resolution techniques with the full-range OCRT framework to achieve higher resolution, expanded field-of-view, and isotropic image reconstruction. By incorporating the inherent sparsity and continuity priors of biological samples, we iteratively refine the initially acquired low-resolution OCT images, enhancing their resolution. This model is integrated into the previously established full-range OCRT framework to enable isotropic super-resolution with expanded field-of-view. In addition, the FROCRT technique leverages multi-angle Fourier synthesis to effectively mitigate reconstruction artifacts that may arise from over-enhancement by the super-resolution model. We applied SC-FROCRT to phantom samples, sparse plant tissues, and cleared biological tissues, achieving the Fourier ring correlation (FRC) metric improved by an average of 1.41 times over FROCRT. We anticipate that SC-FROCRT will broaden the scope of OCT applications, enhancing its utility for both diagnostic and research purposes.

Overcoming the diffraction barrier in long-range optical imaging is recognized as a critical challenge for space situational awareness and terrestrial remote sensing. This study presents a super-resolution imaging method based on reflective tomography LiDAR (RTL), breaking through the traditional optical diffraction limit to achieve 2 cm resolution imaging at a distance of 10.38 km. Aiming at challenges such as atmospheric turbulence, diffraction limits, and data sparsity in long-range complex target imaging, the study proposes the applicable methods of the nonlocal means (NLM) algorithm, combined with a self-developed RTL system to solve the problem of high-precision reconstruction of multi-angle projection data. Experimental results show that the system achieves a reconstruction resolution for complex targets (NUDT WordArt model) that is better than 2 cm, which is 2.5 times higher than the 5 cm diffraction limit of the traditional 1064 nm laser optical system. In sparse data scenarios, the NLM algorithm outperforms traditional algorithms in metrics such as information entropy (IE) and structural similarity (SSIM) by suppressing artifacts and maintaining structural integrity. This study presents the first demonstration of centimeter-level tomographic imaging for complex targets at near-ground distances exceeding 10 km, providing a new paradigm for fields such as space debris monitoring and remote target recognition.