Fourier ptychography (FP) is a promising computational imaging technique that overcomes the physical space-bandwidth product (SBP) limit of a conventional microscope by applying angular-varied illuminations. However, to date, the effective imaging numerical aperture (NA) achievable with a commercial LED board is still limited to the range of 0.3−0.7 with a 4 × /0.1NA objective due to the geometric constraint with the declined illumination intensities and attenuated signal-to-noise ratio (SNR). Thus the highest achievable half-pitch resolution is usually constrained between 500−1000 nm, which cannot meet the requirements of high-resolution biomedical imaging applications. Although it is possible to improve the resolution by using a high-NA objective lens, the FP approach is less appealing as the decrease of field-of-view (FOV) will far exceed the improvement of spatial resolution in this case. In this paper, we initially present a subwavelength resolution Fourier ptychography (SRFP) platform with a hemispherical digital condenser to provide high-angle programmable plane-wave illuminations of 0.95NA, attaining a 4 × /0.1NA objective with the final effective imaging performance of 1.05NA at a half-pitch resolution of 244 nm with the incident wavelength of 465 nm across a wide FOV of 14.60 mm², corresponding to a SBP of 245 megapixels. Our work provides an essential step of FP towards high-throughput imaging applications.

Dual-wavelength in-line digital holography (DIDH) is one of the popular methods for quantitative phase imaging of objects with non-contact and high-accuracy features. Two technical challenges in the reconstruction of these objects include suppressing the amplified noise and the twin-image that respectively originate from the phase difference and the phase-conjugated wavefronts. In contrast to the conventional methods, the deep learning network has become a powerful tool for estimating phase information in DIDH with the assistance of noise suppressing or twin-image removing ability. However, most of the current deep learning-based methods rely on supervised learning and training instances, thereby resulting in weakness when it comes to applying this training to practical imaging settings. In this paper, a new DIDH network (DIDH-Net) is proposed, which encapsulates the prior image information and the physical imaging process in an untrained deep neural network. The DIDH-Net can effectively suppress the amplified noise and the twin-image of the DIDH simultaneously by automatically adjusting the weights of the network. The obtained results demonstrate that the proposed method with robust phase reconstruction is well suited to improve the imaging performance of DIDH.

An algorithm for quantitative reconstruction of the optical thickness distribution of objects is proposed based on single-shot dual-wavelength in-line digital holography. Two single-wavelength holograms can be extracted from a single-shot recorded dual-wavelength in-line hologram. The quantitative optical thickness distribution of the specimen can be reconstructed directly without calculations of the phase images at every single wavelength. Thus, off-axis recording and phase-shifting operation are not required, enabling a fast and high-resolution measurement. The effectiveness and accuracy of the proposed method are verified by both numerical simulations and experimental results.

Light-sheet fluorescence microscopy (LSFM) facilitates high temporal-spatial resolution, low photobleaching and phototoxicity for long-term volumetric imaging. However, when a high axial resolution or optical sectioning capability is required, the field of view (FOV) is limited. Here, we propose to generate a large FOV of light-sheet by scanning multiple focus-shifted Gaussian beam arrays (MGBA) while keeping the high axial resolution. The positions of the beam waists of the multiple Gaussian beam arrays are shifted in both axial and lateral directions in an optimized arranged pattern, and then scanned along the direction perpendicular to the propagation axis to form an extended FOV of light-sheet. Complementary beam subtraction method is also adopted to further improve axial resolution. Compared with the single Gaussian light-sheet method, the proposed method extends the FOV from 12 μm to 200 μm while sustaining the axial resolution of 0.73 μm. Both numerical simulation and experiment on samples are performed to verify the effectiveness of the method.

By exploiting the total variation (TV) regularization scheme and the contrast transfer function (CTF), a phase map can be retrieved from single-distance coherent diffraction images via the sparsity of the investigated object. However, the CTF-TV phase retrieval algorithm often struggles in the presence of strong noise, since it is based on the traditional compressive sensing optimization problem. Here, convolutional neural networks, a powerful tool from machine learning, are used to regularize the CTF-based phase retrieval problems and improve the recovery performance. This proposed method, the CTF-Deep phase retrieval algorithm, was tested both via simulations and experiments. The results show that it is robust to noise and fast enough for high-resolution applications, such as in optical, x-ray, or terahertz imaging.

Wide-field microscopy (WFM) is broadly used in experimental studies of biological specimens. However, combining the out-of-focus signals with the in-focus plane reduces the signal-to-noise ratio (SNR) and axial resolution of the image. Therefore, structured illumination microscopy (SIM) with white light illumination has been used to obtain full-color 3D images, which can capture high SNR optically-sectioned images with improved axial resolution and natural specimen colors. Nevertheless, this full-color SIM (FC-SIM) has a data acquisition burden for 3D-image reconstruction with a shortened depth-of-field, especially for thick samples such as insects and large-scale 3D imaging using stitching techniques. In this paper, we propose a deep-learning-based method for full-color WFM, i.e., FC-WFM-Deep, which can reconstruct high-quality full-color 3D images with an extended optical sectioning capability directly from the FC-WFM z-stack data. Case studies of different specimens with a specific imaging system are used to illustrate this method. Consequently, the image quality achievable with this FC-WFM-Deep method is comparable to the FC-SIM method in terms of 3D information and spatial resolution, while the reconstruction data size is 21-fold smaller and the in-focus depth is doubled. This technique significantly reduces the 3D data acquisition requirements without losing detail and improves the 3D imaging speed by extracting the optical sectioning in the depth-of-field. This cost-effective and convenient method offers a promising tool to observe high-precision color 3D spatial distributions of biological samples.