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.

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.

Non-contact detection of weak, transient acoustic signals is essential for photoacoustic and laser-ultrasound (LUS) bioimaging, including the assessment of corneal deformation and thickness variation. Passive 3 × 3 fiber-coupler interferometers provide phase-diverse sensing without external modulation; however, conventional ellipse-based calibration becomes unreliable when the measured phase excursion covers only a small fraction of the characteristic ellipse, as in nanometer-scale corneal responses. This study presents a physics-guided temporal convolutional network (PG-TCN) that directly maps two nonideal interferometric outputs to a continuous dynamic phase signal without ellipse fitting or separate phase unwrapping during inference. The model combines dilated one-dimensional convolutions with interferometric-consistency and temporal-regularization losses. Tests using PZT-modulated calibration signals acquired under several static chamber pressures of 10, 15, 20, and 25 kPa showed lower mean absolute error, higher normalized cross-correlation, and lower displacement RMSE than a CNN-LSTM baseline. In corneal-phantom LUS measurements, the first detected echo shifted from 4.632 to 4.488 µs between 10 and 20 kPa, corresponding to a relative thickness reduction of approximately 7.8 µm/kPa. The PG-TCN required 210 ± 18 µs per sequence while using approximately half the trainable parameters of the CNN-LSTM baseline. These results demonstrate a fast physics-guided optical detection framework for non-contact sensing of weak acoustic responses, supporting the use of 3 × 3 optical interferometry as a biosensing approach for LUS and photoacoustic applications.

Non-contact ultrasonic thickness measurement is important for industrial metrology, particularly for metallic components where surface temperature, motion, geometry, or contamination can limit conventional contact probes. In this study, we present a fully non-contact thickness measurement system for aluminum plates integrating laser-ultrasound excitation, 3×3 interferometric detection, and deep learning-based phase demodulation. Broadband ultrasonic waves were generated using a pulsed 532 nm laser in the ablation regime, while rear-surface displacement was detected using a 1550 nm fiber-optic interferometric probe. Two phase-shifted outputs from a 3×3 coupler were processed by a neural interferometric demodulator (NID), which directly reconstructed continuous optical phase without separate quadrature reconstruction, arctangent phase extraction, or phase unwrapping during inference. PZT-modulated measurements at 10, 15, 20, and 25 kPa provided additional validation against conventional 3×3 standard ellipse fitting (3×3-SEF), including phase excursions exceeding $2\mathsf{\pi }$. The NID reduced the phase RMSE from 0.048 to 0.071 rad to 0.029–0.041 rad and the processing time from 481 ± 22 μs to 295 ± 30 μs per sequence. For thickness measurement, the reconstructed phase was converted into out-of-plane displacement, and acoustic time-of-flight (TOF) features were extracted. For 2, 6, and 10 mm aluminum plates, the estimated thicknesses were 1.911 ± 0.024 mm, 6.102 ± 0.070 mm, and 10.516 mm with a partial standard uncertainty of 0.127 mm, respectively. These results demonstrate accurate and efficient deep learning-assisted phase demodulation for fully non-contact laser-ultrasonic thickness gauging.