Circular polarizers and their effect on partial coherence.

We examine the action of a circular polarizer on an incident beam that is spatially partially coherent and partially polarized. It is found that the beam's coherence area can be significantly increased or decreased by the polarizer. Furthermore, an expression for the transmission efficiency is derived.

Snapshot dual-wavelength digital holography with LED and laser hybrid illumination.

To address the problem of the time-sharing recording of dual-wavelength low-coherence holograms while avoiding the use of customized achromatic optical elements, a snapshot dual-wavelength digital holography with LED and laser hybrid illumination is proposed. In this method, the parallel phase-shifting method is firstly employed to suppress zero-order and twin-image noise, and to record a LED hologram with low speckle noise and full field of view. Secondly, another laser hologram with a different center wavelength affected by speckle noise is recorded simultaneously using the spatial multiplexing technique. Finally, dual-wavelength wrapped phase images are reconstructed from a spatial multiplexing hologram, and then are combined to achieve low-noise phase unwrapping utilizing the iterative algorithm. Simulation and optical experiments on a reflective step with a depth of 1.38µm demonstrate that the proposed method can achieve single-shot and large-range height measurements while maintaining low-noise and full-field imaging.

Fly-scan high-throughput coded ptychographic microscopy via active micro-vibration and rolling-shutter distortion correction.

Recent advancements in ptychography have demonstrated the potential of coded ptychography (CP) for high-resolution optical imaging in a lensless configuration. However, CP suffers imaging throughput limitations due to scanning inefficiencies. To address this, we propose what we believe is a novel 'fly-scan' scanning strategy utilizing two eccentric rotating mass (ERM) vibration motors for high-throughput coded ptychographic microscopy. The intrinsic continuity of the 'fly-scan' technique effectively eliminates the scanning overhead typically encountered during data acquisition. Additionally, its randomized scanning trajectory considerably reduces periodic artifacts in image reconstruction. We also developed what we believe to be a novel rolling-shutter distortion correction algorithm to fix the rolling-shutter effects. We built up a low-cost, DIY-made prototype platform and validated our approach with various samples including a resolution target, a quantitative phase target, a thick potato sample and biospecimens. The reported platform may offer a cost-effective and turnkey solution for high-throughput bio-imaging.

BLSTM convolution and self-attention network enabled recursive and direct prediction for optical chaos.

Chaotic time series prediction has attracted much attention in recent years because of its important applications, such as security analysis for random number generators and chaos synchronization in private communications. Herein, we propose a BLSTM convolution and self-attention network model to predict the optical chaos. We validate the model's capability for direct and recursive prediction, and the model dramatically reduces the accumulation of errors. Moreover, the time duration prediction of optical chaos is increased with comparative accuracy where the predicted sequence length reaches 4 ns with normalized mean squared error (NMSE) of less than 0.01.

Clinically relevant stratification of patients with type 2 diabetes by using continuous glucose monitoring data.

AIM: The wealth of data generated by continuous glucose monitoring (CGM) provides new opportunities for revealing heterogeneities in patients with type 2 diabetes mellitus (T2DM). We aimed to develop a method using CGM data to discover T2DM subtypes and investigate their relationship with clinical phenotypes and microvascular complications. METHODS: The data from 3119 patients with T2DM who wore blinded CGM at an academic medical centre was collected, and a glucose symbolic pattern (GSP) metric was created that combined knowledge-based temporal abstraction with numerical vectorization. The k-means clustering was applied to GSP to obtain subgroups of patients with T2DM. Clinical characteristics and the presence of diabetic retinopathy and albuminuria were compared among the subgroups. The findings were validated in an independent population comprising 773 patients with T2DM. RESULTS: By using GSP, four subgroups were identified with distinct features in CGM profiles and parameters. Moreover, the clustered subgroups differed significantly in clinical phenotypes, including indices of pancreatic ß-cell function and insulin resistance (all p < .001). After adjusting for confounders, group C (the most insulin resistant) had a significantly higher risk of albuminuria (odds ratio = 1.24, 95% confidence interval: 1.03-1.39) relative to group D, which had the best glucose control. These findings were confirmed in the validation set. CONCLUSION: Subtyping patients with T2DM using CGM data may help identify high-risk patients for microvascular complications and provide insights into the underlying pathophysiology. This method may help refine clinically meaningful stratification of patients with T2DM and inform personalized diabetes care.

Trace photoacoustic SO2 gas sensor in SF6 utilizing a 266†nm UV laser and an acousto-optic power stabilizer.

A sulfur dioxide (SO2) gas sensor based on the photoacoustic spectroscopy technology in a sulfur hexafluoride (SF6) gas matrix was demonstrated for SF6 decomposition components monitoring in the power system. A passive Q-switching laser diode (LD) pumped all-solid-state 266 nm deep-ultraviolet laser was exploited as the laser excitation source. The photoacoustic signal amplitude is linear related to the incident optical power, whereas, a random laser power jitter is inevitable since the immature laser manufacturing technology in UV spectral region. A compact laser power stabilization system was developed for better sensor performance by adopting a photodetector, a custom-made internal closed-loop feedback controller and a Bragg acousto-optic modulator (AOM). The out-power stability of 0.04% was achieved even though the original power stability was 0.41% for ∼ 2 hours. A differential two-resonator photoacoustic cell (PAC) was designed for weak photoacoustic signal detection. The special physical constants of SF6 buffer gas induced a high-Q factor of 85. A detection limit of 140 ppbv was obtained after the optimization, which corresponds to a normalized noise equivalent absorption coefficient of 3.2 × 10-9 cm-1WHz-1/2.

Enhancing polarization 3D facial imaging: overcoming azimuth ambiguity without extra depth devices.

Polarization 3D imaging has been a research hotspot in the field of 3D facial reconstruction because of its biosafety, high efficiency, and simplicity. However, the application of this technology is limited by the multi-valued problem of the azimuth angle of the normal vector. Currently, the most common method to overcome this limitation is to introduce additional depth techniques at the cost of reducing its applicability. This study presents a passive 3D polarization facial imaging method that does not require additional depth-capturing devices. It addresses the issue of azimuth ambiguity based on prior information about the target image's features. Specifically, by statistically analyzing the probability distribution of real azimuth angles, it is found that their quadrant distribution is closely related to the positions of facial feature points. Therefore, through facial feature detection, the polarized normal azimuth angle of each pixel can be accurately assigned to the corresponding quadrant, thus determining a precise unique normal vector and achieving accurate 3D facial reconstruction. Finally, our azimuth angle correction method was validated by simulated polarization imaging results, and it achieved accurate correction for over 75% of the global pixels without using additional depth techniques. Experimental results further indicate that this method can achieve polarization 3D facial imaging under natural conditions without extra depth devices, and the 3D results preserve edge details and texture information.

Structured modulation multi-height microscopy for high-resolution imaging.

Conventional multi-height microscopy techniques introduce different object-to-detector distances to obtain multiple measurements for phase retrieval. However, surpassing the diffraction limit imposed by the numerical aperture (NA) of the objective lens remains a challenging task. Here, we report a novel structured modulation multi-height microscopy technique for quantitative high-resolution imaging. In our platform, a thin diffuser is placed in between the sample and the objective lens. By translating the diffuser to different axial positions, a sequence of modulated intensity images is captured for reconstruction. The otherwise inaccessible high-resolution object information can thus be encoded into the optical system for detection. In the construction process, we report a ptychographic phase retrieval algorithm to recover the existing wavefront of the complex object. We validate our approach using a resolution target, a phase target, and various biological samples. We demonstrate a ∼4-fold resolution gain over the diffraction limit. We also demonstrate our approach to achieve a 6.5 mm by 4.3 mm field of view and a half-pitch resolution of 1.2 µm. The reported methodology provides a portable, turnkey solution for quantitative high-resolution imaging with potential applications in disease diagnosis, sample screening, and other fields.

Complex amplitude field recovery of a scattering media obstructed object with multi-captured images.

An iterative-based method for recovering the complex amplitude field behind scattering media is presented in this Letter. This method compensates the random phase modulation of scattering media by using multiple captured scattered light fields. Complex amplitude reconstruction with local iterative averaging of scattered light fields, and double weighted feedback is efficiently applied. Two feasible types of system setups, with varying detector positions and wavelength, are proposed. Simulations and proof-of-concept experiments are employed to demonstrate the effectiveness of the proposed method in reconstructing complex amplitude of a hidden target.

Multi-target distortion correction in 3D shape from polarization using a monocular camera system by deep neural networks.

The shape from polarization is a noncontact 3D imaging method that shows great potential, but its application is limited by the monocular camera system and surface integration algorithm. This Letter proposes a novel, to the best of our knowledge, method that employs deep neural networks to enhance multi-target 3D reconstruction, making a significant advancement in the field. By constructing the relationship between targets' blur, distance, and clarity, the proposed method provides accurate spatial information while mitigating inaccuracies arising from the continuous model. Experiments show that the constructed neural network can help improve the multi-target 3D reconstruction quality compared with conventional methods.

Low-cost and simple optical system based on wavefront coding and deep learning.

With the development of computational imaging, the integration of optical system design and digital algorithms has made more imaging tasks easier to perform. Wavefront coding (WFC) is a typical computational imaging technique that is used to address the constraints of optical aperture and depth of field. In this paper, we demonstrated a low-cost and simple optical system based on WFC and deep learning. We constructed an optimized encoding method for the phase plate under the framework of deep learning, which reduces the requirement for aberration correction in the full field of view. Optical coding was achieved with just a double-bonded lens and a simple cubic phase mask, and digital decoding used the deep residual UNet++ network framework. The final image obtained has good resolution, whereas the depth of field of the system expanded by a factor of 13, which is of great significance for the high-precision inspection and attaching of small parts of machine vision.

Monocular polarized three-dimensional absolute depth reconstruction technology for multi-target scenes.

The traditional polarization three-dimensional (3D) imaging technology has limited applications in the field of vision because it can only obtain the relative depth information of the target. Based on the principle of polarization stereo vision, this study combines camera calibration with a monocular ranging model to achieve high-precision recovery of the target's absolute depth information in multi-target scenes. Meanwhile, an adaptive camera intrinsic matrix prediction method is proposed to overcome changes in the camera intrinsic matrix caused by focusing on fuzzy targets outside the depth of field in multi-target scenes, thereby realizing monocular polarized 3D absolute depth reconstruction under dynamic focusing of targets at different depths. Experimental results indicate that the recovery error of monocular polarized 3D absolute depth information for the clear target is less than 10%, and the detail error is only 0.19 mm. Also, the precision of absolute depth reconstruction remains above 90% after dynamic focusing on the blurred target. The proposed monocular polarized 3D absolute depth reconstruction technology for multi-target scenes can broaden application scenarios of the polarization 3D imaging technology in the field of vision.

Estimation and removal of backscattered light with nonuniform polarization information in underwater environments.

The nonuniform of polarization information of backscattered light has always been a neglected characteristic in polarization underwater imaging, but its accurate estimation plays an important role in the quality of imaging results. Traditional polarization imaging methods assume that the degree of polarization and angle of polarization of backscattered light are constant. In fact, the polarization information of backscattering light is gradual, this assumption makes traditional methods work only in a small area of the camera's field of view, in which the change of the polarization information of backscattered light can be ignored. In this paper, by analyzing the distribution of backscattered light, it is concluded that its polarization information has the characteristics of low-rank. Then, the degree of polarization and angle of polarization of backscattered light were estimated by low-rank and sparse matrix decomposition, and the clear scene was reconstructed. Experimental results show that the proposed method breaks through the limitation of the assumption of backscattered light in traditional polarization imaging method, and expands the detection field under the same conditions, which makes it possible to develop polarization underwater imaging method to the direction of large field of view detection.

Impact of color on polarization-based 3D imaging and countermeasures.

Diffuse polarization-based 3D imaging has flourished with the ability to obtain the 3D shapes of objects without multiple detectors, active mode lighting, or complex mechanical structures, which are major drawbacks of other methods for 3D imaging in natural scenes. However, traditional polarization-based 3D imaging technology introduces color distortion when reconstructing the surface of multi-colored targets. We propose a polarization-based 3D imaging model to recover the 3D geometry of multi-colored Lambertian objects. In particular, chromaticity-based color removal theory is used to restore the intrinsic intensity, which is modulated only by the target shape, and we apply the recovered intrinsic intensity to address the orientation uncertainty of target normals due to azimuth ambiguity. Finally, we integrate the corrected normals to reconstruct high-precision 3D shapes. Experimental results demonstrate that the proposed model has the ability to reconstruct multi-colored Lambertian objects exhibiting non-uniform reflectance from single views under natural light conditions.

Computational optical system design: a global optimization method in a simplified imaging system.

An optical imaging system often has problems of high complexity and low energy transmittance to compensate for aberrations. Here we propose a method to correct aberrations by coupling an optical subsystem with a digital subsystem. Specifically, in the global optimization process, the two subsystems correct their respective, easily handled aberrations so that the final imaging aberration is minimized. We design simple lenses with this method and assess imaging quality. In addition, we conduct a tolerance analysis for the proposed method and verify the effectiveness of deconvolution using a spatially varying point spread function (SVPSF) in the actual imaging process. Simulation results show the superiority of the proposed method compared with the conventional design and the feasibility of simplifying the optical system. Experimental results prove the effectiveness of deconvolution using SVPSF.

Enhancement of underwater vision by fully exploiting the polarization information from the Stokes vector.

Underwater imaging method based on polarization information is extremely popular due to its ability to effectively remove the backscattered light. The Stokes vector contains the information of both the degree and angle of polarization of the light wave. However, this aspect has been rarely utilized in image reconstruction. In this study, an underwater polarimetric imaging model is established by fully exploiting this feature of Stokes vectors. The transmission of light wave is described in terms of the polarization information derived from the Stokes vector. Then, an optimization function is designed based on the independent characteristics of target light and backscattered light to estimate the target and backscattered field information. The real-world experiments and mean squared error analysis verify that the proposed method can remove the backscattered light and recover the target information accurately.

Near-infrared monocular 3D computational polarization imaging of surfaces exhibiting nonuniform reflectance.

This paper presents a near-infrared (NIR) monocular 3D computational polarization imaging method to directly reconstruct the shape of surfaces exhibiting nonuniform reflectance. A reference gradient field is introduced to the weight constraints for globally correcting the ambiguity of the surface normal for a target with nonuniform reflectance. We experimentally demonstrated that our method can reconstruct the shape of surfaces exhibiting nonuniform reflectance in not only the near field but also the far field. Moreover, with the proposed method, the axial resolution can be kept constant even under different object distances as long as the ratio of the focal length to the object distance is fixed. The simplicity and robustness of the proposed method make it an attractive tool for the fast modeling of 3D scenes.

Near-infrared laser photoacoustic gas sensor for simultaneous detection of CO and H2S.

A ppb-level H2S and CO photoacoustic spectroscopy (PAS) gas sensor was developed by using a two-stage commercial optical fiber amplifier with a full output power of 10 W. Two near-infrared diode lasers with the central wavenumbers of 6320.6 cm-1 and 6377.4 cm-1 were employed as the excitation laser source. A time-division multiplexing method was used to simultaneously detect CO and H2S with an optical switch. A dual-resonator structural photoacoustic cell (PAC) was theoretically simulated and designed with a finite element analysis. A µV level background noise was achieved with the differential and symmetrical PAC. The performance of the multi-component sensor was evaluated after the optimization of frequency, pressure and modulation depth. The minimum detection limits of 31.7 ppb and 342.7 ppb were obtained for H2S and CO at atmospheric pressure.

Deep learning-enabled whole slide imaging (DeepWSI): oil-immersion quality using dry objectives, longer depth of field, higher system throughput, and better functionality.

Whole slide imaging (WSI) has moved the traditional manual slide inspection process to the era of digital pathology. A typical WSI system translates the sample to different positions and captures images using a high numerical aperture (NA) objective lens. Performing oil-immersion microscopy is a major obstacle for WSI as it requires careful liquid handling during the scanning process. Switching between dry objective and oil-immersion lens is often impossible as it disrupts the acquisition process. For a high-NA objective lens, the sub-micron depth of field also poses a challenge to acquiring in-focus images of samples with uneven topography. Additionally, it implies a small field of view for each tile, thus limiting the system throughput and resulting in a long acquisition time. Here we report a deep learning-enabled WSI platform, termed DeepWSI, to substantially improve the system performance and imaging throughput. With this platform, we show that images captured with a regular dry objective lens can be transformed into images comparable to that of a 1.4-NA oil immersion lens. Blurred images with defocus distance from -5 µm to +5 µm can be virtually refocused to the in-focus plane post measurement. We demonstrate an equivalent data throughput of >2 gigapixels per second, the highest among existing WSI systems. Using the same deep neural network, we also report a high-resolution virtual staining strategy and demonstrate it for Fourier ptychographic WSI. The DeepWSI platform may provide a turnkey solution for developing high-performance diagnostic tools for digital pathology.

Single-shot imaging through scattering media under strong ambient light interference.

Speckle correlation imaging (SCI) has found tremendous versatility compared with other scattering imaging approaches due to its single-shot data acquisition strategy, relatively simple optical setup, and high-fidelity reconstruction performance. However, this simplicity requires SCI experiments to be performed strictly in a darkroom condition. As background noise increases, the speckle contrast rapidly decreases, making precise interpretation of the data extremely difficult. Here, we demonstrate a method by refining the speckle in the autocorrelation domain to achieve high-performance single-shot imaging. Experiment results prove that our method is adapted to estimate objects in a low signal-to-background ratio (SBR) circumstance even if the SBR is about -23dB. Laboratory and outdoor SCI experiments are performed.