Microsphere-based subwavelength imaging technique was first demonstrated in 2011. After nearly a decade of efforts, such technique has spawned numerous interests in fields such as laser nano-machining, imaging, sensing, and biological detection. For wider industrial-scale application of the technique, a robust and low-cost objective lens incorporating a microsphere lens is highly desired and sought by many researchers. In this work, we demonstrate a unibody microscope objective lens formed by tipping a high-index microsphere onto a plano-convex lens and subsequently fitting them into a conventional objective lens. We call this the plano-convex-microsphere (PCM) objective, which resembles the appearance and operation of an ordinary microscope objective while providing super-resolving power in discerning subwavelength 100 nm features ( λ / 4 . 7 ) in air and far-field conditions. The imaging performance of the PCM objective, along with the working distance, has been systematically investigated. It has a calibrated resolution of λ / 3 in the far field, a numerical aperture of 1.57, and a working distance of 3.5 µm. With the assistance of a scanning process, larger-area imaging is realized. The PCM objective can be easily adapted to existing microscope systems and is appealing for commercialization.

Using a single multimode fiber for both illumination and imaging offers notable advantages in developing ultra-thin imaging probes. However, uneven speckle illumination introduces additional noise, complicating high-precision reconstruction of complex grayscale images, which remains challenging for traditional methods. In this study, we first optimize the image reconstruction framework by combining the inverse transmission matrix approach with deep neural networks, enhancing interpretability and delivering exceptional performance in reconstructing complex images. To address the noise introduced by uneven speckle illumination, we increase the target exposure and effectively integrate information from multiple illumination conditions. Results show that our proposed Multi-speckle Illumination type Inverse Transmission Matrix-Unet (MITM-Unet) method significantly outperforms the Single-speckle illumination type (SITM-Unet). Specifically, images reconstructed with MITM-Unet achieve a structural similarity index of 0.59 and a Pearson correlation coefficient of 0.91, compared to SITM-Unet’s 0.38 and 0.77. These findings underscore the effectiveness of the MITM-Unet method in achieving high-quality imaging of complex grayscale targets, providing valuable insights into the imaging capabilities of single multimode fiber systems. This work holds promise for advancing simpler, more compact wide-field endomicroscopic imaging using multimode fibers.

Fiber bundle endomicroscopy has potential for facilitating high-resolution (HR) in vivo imaging. One of the main challenges of this technique is the improvement of image restoration for better visualization. In this paper, we propose to reconstruct a HR image without a fixed honeycomb pattern from a noisy observation image, which can be formulated as an inverse problem. We use the obtained fixed honeycomb pattern as a prior image and use a two-step iterative shrinkage thresholding algorithm with a total variation regularization to solve this problem. In addition to the fixed honeycomb pattern removal, our method can also improve spatial resolution. The feasibility of our method is demonstrated by the images obtained from the USAF target and spider silks. In each ease, our method recovers more details than that recovered by the conventional method. The proposed theoretical framework for the removal of the honeycomb pattern in this paper shows promising and wide applications for fiber bundle imaging.

In recent years, multimode fiber (MMF) has emerged as a focal point in ultrathin endoscopy owing to its high-capacity information transmission. Nevertheless, the technology's susceptibility to external perturbances limits its practical applications. In this study, we employ a single MMF as both the illumination unit and imaging probe and utilize this single-shot wide-field MMF imaging system to investigate the impact of LED and laser sources on anti-perturbation capabilities. Experimental results demonstrate that, in the absence of deformations in the MMF, both LED and laser-based systems achieve an average structural similarity (SSIM) index of around 0.8 for the reconstructed image, utilizing advanced deep learning techniques, with the laser-based system performing slightly better. However, under unknown MMF configurations post-deformation, the SSIM remains robust at 0.67 for the LED-based system, while the laser-based system drops the average SSIM to 0.45. The results reveal that LED has anti-perturbation capability in single-shot wide-field MMF imaging systems. These findings indicate significant potential for future anti-perturbation studies in endoscopy employing MMF imaging.

A fiber bundle allows easy access to a wide range of human tissue. It has smaller diameters, which limits its effective field of view (FOV), and consists of a large number of cores surrounded by a cladding layer, which reduces its spatial resolution. In this paper, we develop an algorithm that processes successively captured raw fiber bundle images in an online fashion. Our algorithm tackles the tasks of super-resolution (SR) and video mosaicing jointly. The natural movement of the fiber tip in successive frames produces offsets that are random in the pixel domain to apply multi-frame SR imaging. Meanwhile, the associated FOV can be extended by mosaicing reconstructed SR images with obtained shifted information. Our approach has low computational complexity that allows for processing in real-time. The performance of resolution improvement in real-time and video mosaicing is demonstrated on the resolution target and biological samples.

Differential phase contrast (DPC) imaging relies on computational analysis to extract quantitative phase information from phase gradient images. However, even modest noise level can introduce errors that propagate through the computational process, degrading the quality of the final phase result and further reducing phase sensitivity. Here, we introduce the noise-corrected DPC (ncDPC) to enhance phase sensitivity. This approach is based on a theoretical DPC model that effectively considers most relevant noise sources in the camera and non-uniform illumination in DPC. In particular, the dominating shot noise and readout noise variance can be jointly estimated using frequency analysis and further corrected by block-matching 3D (BM3D) method. Finally, the denoised images are used for phase retrieval based on the common Tikhonov inversion. Our results, based on both simulated and experimental data, demonstrate that ncDPC outperforms the traditional DPC (tDPC), enabling significant improvements in both phase reconstruction quality and phase sensitivity. Besides, we have demonstrated the broad applicability of ncDPC by showing its performance in various experimental datasets.