Energy-efficient dispersion compensation for digital micromirror device.

Due to the wave nature of light, the diffraction pattern generated by an optical device is sensitive to the shift of wavelength. This fact significantly compromises the digital micromirror device (DMD) in applications, such as full-color holographic display and multi-color fluorescence microscopy. The existing dispersion compensation techniques for DMD involve adding diffractive elements, which causes a large amount of waste of optical energy.

High-fidelity multi-channel optical information transmission through scattering media.

High-fidelity optical information transmission through strongly scattering media is challenging, but is crucial for the applications such as the free-space optical communication in a haze or fog. Binarizing optical information can somehow suppress the disruptions caused by light scattering. However, this method gives a compromised communication throughput.

Multi-view stitching phase measuring deflectometry for freeform specular surface metrology.

Phase measuring deflectometry (PMD) offers notable advantages for precision inspection of specular elements. Nevertheless, if confronts challenges when measuring freeform specular surfaces due to the dispersion of reflection rays from surfaces with high local slopes. Here, we propose a multi-view stitching PMD.

High-frame-rate reconfigurable diffractive neural network based on superpixels.

The existing implementations of reconfigurable diffractive neural networks rely on both a liquid-crystal spatial light modulator and a digital micromirror device, which results in complexity in the alignment of the optical system and a constrained computational speed. Here, we propose a superpixel diffractive neural network that leverages solely a digital micromirror device to control the neuron bias and connection. This approach considerably simplifies the optical system and achieves a computational speed of 326 Hz per neural layer.

Classification of breast cancer histology images using MSMV-PFENet.

Deep learning has been used extensively in histopathological image classification, but people in this field are still exploring new neural network architectures for more effective and efficient cancer diagnosis. Here, we propose multi-scale, multi-view progressive feature encoding network (MSMV-PFENet) for effective classification. With respect to the density of cell nuclei, we selected the regions potentially related to carcinogenesis at multiple scales from each view.

Anti-scattering light focusing with full-polarization digital optical phase conjugation based on digital micromirror devices.

The high resolution of optical imaging and optogenetic stimulation in the deep tissue requires focusing light against strong scattering with high contrast. Digital optical phase conjugation (DOPC) has emerged recently as a promising solution for this requirement, because of its short latency. A digital micromirror device (DMD) in the implementation of DOPC enables a large number of modulation modes and a high speed of modulation both of which are important when dealing with a highly dynamic scattering medium.

Anti-scattering light focusing by fast wavefront shaping based on multi-pixel encoded digital-micromirror device.

Speed and enhancement are the two most important metrics for anti-scattering light focusing by wavefront shaping (WS), which requires a spatial light modulator with a large number of modulation modes and a fast speed of response. Among the commercial modulators, the digital-micromirror device (DMD) is the sole solution providing millions of modulation modes and a pattern rate higher than 20 kHz. Thus, it has the potential to accelerate the process of anti-scattering light focusing with a high enhancement.