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.

Complex diffusive scattering media pose significant challenges for light focusing as well as optical imaging to be implemented in practice. Recently, it has been demonstrated that the wavefront shaping technique can be applied to realize focusing and imaging through scattering medium. Here we report dynamic optical manipulation of particles through turbid media by employing the interleaved segment wavefront correction method, which is an improved genetic algorithm providing faster convergence speed and higher peak to background ratio. Manipulating micro-beads behind a scattering medium along both one and two dimensional predesigned trajectories in real time has been successfully demonstrated.

Optical manipulation with nondiffracting beams has been attracting great interest and finding widespread applications in many fields such as chemistry, physics, and biomedicine. Generally, optical manipulation is conducted in an optical microscopy system, which, in general, only allows for imaging motions of particles in the transverse plane, rendering the observation of dynamics processes occurring in the axial plane impractical. We propose and demonstrate an optical manipulation system that incorporates an axial plane imaging module. With this system, the trapping behavior in the transverse plane and the transportation process in the axial plane of a particle immersed in a Bessel beam were acquired simultaneously in real time.

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.

The scattering effect occurring when light passes through inhomogeneous-refractive-index media such as atmosphere or biological tissues will scramble the light wavefront into speckles and impede optical imaging. Wavefront shaping is an emerging technique for imaging through scattering media that works by addressing correction of the disturbed wavefront. In addition to the phase and amplitude, the polarization of the output scattered light will also become spatially randomized in some cases. The recovered image quality and fidelity benefit from correcting as much distortion of the scattered light as possible. Liquid-crystal spatial light modulators (LC-SLMs) are widely used in the wavefront shaping technique, since they can provide a great number of controlled modes and thereby high-precision wavefront correction. However, due to the working principle of LC-SLMs, the wavefront correction is restricted to only one certain linear polarization state, resulting in retrieved image information in only the right polarization, while the information in the orthogonal polarization is lost. In this paper, we describe a full-polarization wavefront correction system for shaping the scattered light wavefront in two orthogonal polarizations with a single LC-SLM. The light speckles in both polarizations are corrected for retrieval of the full polarization information and faithful images of objects. As demonstrated in the experiments, the focusing intensity can be increased by full-polarization wavefront correction, images of objects in arbitrary polarization states can be retrieved, and the polarization state of the object’s light can also be recognized.