A method to enhance laser ultrasound (LUS) image reconstruction with the time-domain synthetic aperture focusing technique (T-SAFT) is presented, in which the acoustic velocity is extracted in situ with curve fitting. The operational principle is provided with the help of a numerical simulation, and the confirmation is provided experimentally. In these experiments, an all-optic LUS system was developed by using lasers for both excitation and detection of ultrasound. The acoustic velocity of a specimen was extracted in situ by fitting a hyperbolic curve to its B-scan image. The needle-like objects embedded within a polydimethylsiloxane (PDMS) block and a chicken breast have been successfully reconstructed using the extracted in situ acoustic velocity. Experimental results suggest that knowing the acoustic velocity in the T-SAFT process is important not only in finding the depth location of the target object but also for producing a high resolution image. This study is expected to pave the wave to the development and usage of all-optic LUS for bio-medical imaging.

A hemispherical resonator gyroscope (HRG) has been implemented by using a consumer wineglass as the resonator and 3 × 3 optical interferometers as the detectors. The poorness of the off-the-shelf wineglass as the resonator can be overcome by the high performance of the optical interferometer. The effects of asymmetries in stiffness and absorption of the resonator are analyzed theoretically and confirmed experimentally. We prove that the trace of the amplitude ratio of two n = 2 fundamental resonant modes of the resonator follows a straight line in a complex plane. By utilizing the straightness of the ratio and the high performance of the optical interferometer, we extract four real constant parameters characterizing the HRG system. Experimentally, by using a resonator having an average resonance frequency of 444 Hz and Q value of 1477.2, it was possible to measure the Coriolis force at the level of industrial grade. The bias stability was measured as small as 2.093°/h.

Photoacoustic tomography (PAT) combines high optical contrast with deep acoustic penetration, making it valuable for biomedical imaging. However, all-optical systems often face challenges in measuring the acoustic wave-induced displacements on rough and dynamic tissues surfaces. We present an all-optical PAT system enabling fast and high-resolution volumetric imaging in vivo. By integrating holographic microscopy with a soft cover layer and coherent averaging, the system detects ultrasound-induced surface displacements over a 10 × 10 mm² area with 0.5 nm sensitivity in 1 s. A novel backpropagation algorithm reconstructs a depth-selective pressure image from two consecutive displacement maps. The coherent summation of these depth-selective pressure images enables the reconstruction of a 3D acoustic pressure image. Using adaptive multilayer backpropagation, we achieve imaging depths of up to 5 mm, with lateral and axial resolutions of 158 µm and 92 µm, respectively, demonstrated through in vivo imaging of mouse vasculature and chicken embryo vessels.

Phase wrapping is a common phenomenon in optical full-field imaging or measurement systems. It arises from large phase retardations and results in wrapped-phase maps that contain essential information about surface roughness and topology. However, these maps are often degraded by noise, such as speckle and Gaussian, which reduces the measurement accuracy and complicates phase reconstruction. Denoising such data is a fundamental problem in computer vision and plays a critical role in biomedical imaging modalities like Full-Field Optical Interferometry. In this paper, we propose WPD-Net (Wrapped-Phase Denoising Network), a lightweight deep learning-based neural network specifically designed to restore phase images corrupted by high noise levels. The network architecture integrates a shallow feature extraction module, a series of Residual Dense Attention Blocks (RDABs), and a dense feature fusion module. The RDABs incorporate attention mechanisms that help the network focus on critical features and suppress irrelevant noise, especially in high-frequency or complex regions. Additionally, WPD-Net employs a growth-rate-based feature expansion strategy to enhance multi-scale feature representation and improve phase continuity. We evaluate the model’s performance on both synthetic and experimentally acquired datasets and compare it with other state-of-the-art deep learning-based denoising methods. The results demonstrate that WPD-Net achieves superior noise suppression while preserving fine structural details even with mixed speckle and Gaussian noises. The proposed method is expected to enable fast image processing, allowing unwrapped biomedical images to be retrieved in real time.

An integrating-bucket method is widely used as a reconstruction tool for full-field optical coherence tomography (FF-OCT). However, it requires high-precision adjustments of the phase modulation parameters. If the parameters are not optimal, the reconstructed tomogram will incur severe artifacts. We propose a post-processing method for removing or reducing such artifacts by utilizing a correction factor extracted from a pre-reconstructed tomogram. FF-OCT imaging created using a coin verifies the effectiveness of the method not only for a single en face image but also for an entire 3-D image. It is expected that the proposed method will expand the application of FF-OCT from biomedical imaging to semiconductor wafers or display panel inspections.