On the Cover
Full-color monolithic hybrid quantum dot nanoring micro light-emitting diodes with improved efficiency using atomic layer deposition and nonradiative resonant energy transfer
Photonics ResearchVol. 7, Issue 4
On the Cover
Free-space propagation of autofocusing Airy vortex beams with controllable intensity gradients
Chinese Optics LettersVol. 17, Issue 4
On the Cover
Fringe pattern analysis using deep learning
Advanced PhotonicsVol. 1, Issue 2
13-17 April, 2020
06- 09 Aug, 2019
Photonic tractor beams: a review
Usually, an unfocused light beam, such as a paraxial Gaussian beam, can exert a force on an object along the direction of light propagation, which is known as light pressure. Recently, however, it was found that an unfocused light beam can also exert an optical pulling force (OPF) on an object toward the source direction; the beam is accordingly named an optical tractor beam. In recent years, this intriguing force has attracted much attention and a huge amount of progress has been made both in theory and experiment. We briefly review recent progress achieved on this topic. We classify the mechanisms to achieve an OPF into four different kinds according to the dominant factors. The first one is tailoring the incident beam. The second one is engineering the object’s optical parameters. The third one is designing the structured material background, in which the light–matter interaction occurs, and the fourth one is utilizing the indirect photophoretic force, which is related to the thermal effect of light absorption. For all the methods, we analyze the basic principles and review the recent achievements. Finally, we also give a brief conclusion and an outlook on the future development of this field.
Advanced Photonics cover story（Vol.2）
In many optical metrology techniques, fringe pattern analysis is the central algorithm for recovering the underlying phase distribution from the recorded fringe patterns. Despite extensive research efforts for decades, how to extract the desired phase information, with the highest possible accuracy, from the minimum number of fringe patterns remains one of the most challenging open problems. Inspired by recent successes of deep learning techniques for computer vision and other applications, we demonstrate for the first time, to our knowledge, that the deep neural networks can be trained to perform fringe analysis, which substantially enhances the accuracy of phase demodulation from a single fringe pattern. The effectiveness of the proposed method is experimentally verified using carrier fringe patterns under the scenario of fringe projection profilometry. Experimental results demonstrate its superior performance, in terms of high accuracy and edge-preserving, over two representative single-frame techniques: Fourier transform profilometry and windowed Fourier transform profilometry.