This study shows that convolutional neural networks (CNNs) can be used to improve the performance of structured illumination microscopy to enable it to reconstruct a super-resolution image using three instead of nine raw frames, which is the standard number of frames required to this end. Owing to the isotropy of the fluorescence group, the correlation between the high-frequency information in each direction of the spectrum is obtained by training the CNNs. A high-precision super-resolution image can thus be reconstructed using accurate data from three image frames in one direction. This allows for gentler super-resolution imaging at higher speeds and weakens phototoxicity in the imaging process.

Conventionally, a digital micromirror device (DMD) can only perform binary amplitude modulation of a light field. Simultaneous amplitude and phase modulation with a DMD is achieved by our proposed error diffusion scheme with a 4f double-lens setup for the first time. The DMD pixels are encoded by adaptive global optimization of binarization errors. In holographic projection, the object image can be optically reconstructed from a complex-amplitude hologram with this scheme. Experimental results show that our proposed error diffusion scheme significantly outperforms the previous superpixel scheme in terms of the image quality and light efficiency of holographic reconstruction results.

Digital holography has high potentials for future 3D imaging and display technology. We present a method for a dynamic full-color digital holographic 3D display on single digital micro-mirror device (DMD) with full-color, high-speed and high-fidelity characteristics. We combine the square regions of adjacent micro-mirrors into super-pixels that can modulate amplitude and phase independently. Gray images are achieved by amplitude modulation and precise positioning of each color is achieved by phase modulation. The proposed method realizes a full-color imaging based on the three primary colors and achieves measured structural similarity of more than 88% and color similarity of more than 98%, while retaining the high switch speed of 9 kHz, thus achieving dynamic full-color 3D display on charge-coupled device (CCD).

Photoacoustic microscopy (PAM) enables the measurement of properties associated with optical absorption within tissues and complements sophisticated technologies employing optical microscopy. An inadequate frequency response as determined by a piezoelectric ultrasonic transducer results, however, in poor depth resolution and inaccurate measurements of the coefficients of optical absorption. We developed a PAM system configured as an attenuated total reflectance sensor with a ten-layer graphene film sandwiched between a prism and water (the coupling medium) for photoacoustic (PA) wave detection. Transients of the PA pressure cause perturbations in the refractive index of the water thereby changing the polarization-dependent absorption of the graphene film. The signal in PA detection involves recording the difference in the temporal-varying reflectance intensity between the two orthogonally polarized probe beams. The graphene-based sensor has an estimated noise-equivalent-pressure sensitivity of ∼550 Pa over an approximately linear pressure response from 11.0 kPa to 55.0 kPa. Moreover, it enables a much broader PA bandwidth detection of up to ∼150 MHz, primarily dominated by a highly localized evanescent field. From the strong optical absorption of inherent hemoglobin, in vivo label-free PAM imaging provided a three-dimensional viewing of the microvasculature of a mouse ear. These results suggest great potential for graphene-based PAM in biomedical investigations, such as microcirculation studies.

Structured illumination microscopy (SIM), as a flexible tool, has been widely applied to observing subcellular dynamics in live cells. It is noted, however, that SIM still encounters a problem with theoretical resolution limitation being only twice over wide-field microscopy, where imaging of finer biological structures and dynamics are significantly constrained. To surpass the resolution limitation of SIM, we developed an image postprocessing method to further improve the lateral resolution of SIM by an untrained neural network, i.e., deep resolution-enhanced SIM (DRE-SIM). DRE-SIM can further extend the spatial frequency components of SIM by employing the implicit priors based on the neural network without training datasets. The further super-resolution capability of DRE-SIM is verified by theoretical simulations as well as experimental measurements. Our experimental results show that DRE-SIM can achieve the resolution enhancement by a factor of about 1.4 compared with conventional SIM. Given the advantages of improving the lateral resolution while keeping the imaging speed, DRE-SIM will have a wide range of applications in biomedical imaging, especially when high-speed imaging mechanisms are integrated into the conventional SIM system.