Phase unwrapping is an important but challenging issue in phase measurement. Even with the research efforts of a few decades, unfortunately, the problem remains not well solved, especially when heavy noise and aliasing (undersampling) are present. We propose a database generation method for phase-type objects and a one-step deep learning phase unwrapping method. With a trained deep neural network, the unseen phase fields of living mouse osteoblasts and dynamic candle flame are successfully unwrapped, demonstrating that the complicated nonlinear phase unwrapping task can be directly fulfilled in one step by a single deep neural network. Excellent anti-noise and anti-aliasing performances outperforming classical methods are highlighted in this paper.

Making heat stand still: Dissipative oscillating systems (waves) can be described mathematically in terms of non-Hermitian physics. When parity-time symmetric systems have dissipative components, the interplay between gain and loss can lead to unusual and exotic behavior. Li et al. show theoretically and demonstrate experimentally that such behavior need not be limited to wave systems. Looking at the diffusion of heat, they devised an experimental setup comprising two thermally coupled disks rotating in opposite directions. The thermal energy transported by each disk is strongly coupled to the disk rotating in the opposite direction, providing a return path for the heat wave. For a particular rotation rate, there is an exceptional point where thermal coupling and counterrotating motion balance, resulting in the thermal energy profile being stationary over time. Science , this issue p. 170

Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and provide an outlook on how to better use DL to improve the reliability and efficiency of PR. Furthermore, we present a live-updating resource (https://github.com/kqwang/phase-recovery) for readers to learn more about PR.

In this Letter, for the first time, to the best of our knowledge, we propose a digital holographic reconstruction method with a one-to-two deep learning framework (Y-Net). Perfectly fitting the holographic reconstruction process, the Y-Net can simultaneously reconstruct intensity and phase information from a single digital hologram. As a result, this compact network with reduced parameters brings higher performance than typical network variants. The experimental results of the mouse phagocytes demonstrate the advantages of the proposed Y-Net.

We propose a new type of noncanonical optical vortex, named “power-exponent-phase vortex (PEPV)”. The spiral focusing of the autofocusing Airy beams carrying PEPVs are experimentally demonstrated, and the physical mechanism is theoretically analyzed by using the energy flow and far field mapping. In addition, the influences of the parameters of PEPVs on the focal fields and orbital angular momenta are also discussed. It is expected that the proposed PEPVs and the corresponding conclusions can be useful for the extension applications of optical vortices, especially for particle trapping and rotating.