Phase distortion compensation via principal component analysis (PCA) is less effective in the extraction of phase distortion from large-step surfaces. In this study, a local principal component analysis method for phase distortion compensation (LPCA) is proposed for phase aberration compensation when measuring large-step objects using dual-wavelength digital holography. The developed approach first extends axial measurement ranges through wavelength multiplexing, then employs PCA to extract localized phase distortions in background regions. A global distortion field is subsequently reconstructed via fitting.

Abstract Traditional optical data storage (ODS) systems face constraints imposed by the optical diffraction limit. It prevents reliable recognition of closely spaced symbols, forcing heavy reliance on error correction codes (ECC) and capping storage density and efficiency. Here, we introduce a deep-learning enhanced computational decoding strategy (DECODS) that treats optical readout as a data-driven optical read-channel model and performs parallel multi-length (2 T–8 T) learned decisions, extending the effective decoding capability beyond the conventional diffraction-limited threshold at the decision level while retaining the standard Blu-ray optical front end. A high-precision physics-based channel model—including aberrations/defocus/tilt, clock/servo jitter, white noise, intersymbol interference and inter-track crosstalk—generates physically consistent training data. Under the validated experimental conditions of this study, DECODS exhibited 0% raw bit-error rate (BER) on evaluated segments of real readout; this does not represent a universal operating guarantee. DECODS also enabled reliable recognition of symbol spacings approaching one quarter of the conventional diffraction-limited threshold at the decision level. This lowers the raw BER, reduces the redundancy required to achieve a target reliability level, and indicates a potential effective capacity improvement of up to 14.8% while supporting tighter symbol/track spacing. Results show a 5.8 × increase in noise tolerance and ≥ 20% expansion of robust servo margins; combined with ECC-related gains, the theoretical effective-density improvement reaches up to 26.28%. DECODS integrates optical physics, computational modeling, and deep learning to provide a scalable decoding framework with quantified, decision-level strategy to longstanding ODS bottlenecks.

Ultraviolet (UV) communication has garnered considerable attention as a method utilizing UV particle scattering for signal transmission. However, the significant path loss due to scattering presents challenges in maintaining signal quality in dynamic communication scenarios. Increasing the emitted signal power is not a viable solution due to safety regulations on radiated energy. Therefore, adaptive power control mechanisms are essential. In this paper, we develop a radiation model for UV LED arrays and propose a power optimization method tailored for UV LED arrays in linear communication links. This method accounts for variables such as communication distance, the number of active LEDs, and the transmit power of each LED, integrating both line-of-sight (LOS) and non-line-of-sight (NLOS) components into the receiver power calculation. We formulate an optimization problem with constraints on bit error rate (BER) and control parameters, aiming to minimize transmit power and beam coverage area. The particle swarm optimization (PSO) algorithm is employed to solve this problem. The results indicate that the model we proposed is able to reduce the signal-to-noise ratio (SNR) by 3.05 dB to 4.06 dB, thereby reducing the transmit power requirements. With BER constraints, the proposed power control strategy ensures energy stabilization across a broad range, with the maximum energy overshoot during distance variations being 41.22%. Compared to power control methods in visible light communication (VLC) systems, the method proposed in this paper is more aligned with the channel characteristics of UV communication. Building on traditional constant-power UV communication systems, the method proposed in this paper can reduce the transmit power by up to 84.24%.

Fast computer-generated holography (CGH) calculation is a critical issue in holographic three-dimensional (3D) display. Analytical methods can be used to generate high-precision holograms but the amount of calculation is very heavy. Deep-learning-based CGH methods have made significant progress but they are difficult to deal with 3D point-cloud object model. We propose a novel scheme that combines a simplified analytical method, accurate phase-added stereograms (APAS), with Complex-Valued Convolutional Neural Network (CCNN) to achieve fast generation of holograms from 3D point-clouds. By using APAS to fast generate low-precision holograms from point-clouds, and then using CCNN to restore the degraded holograms, the APAS algorithm can improve the computational speed of hologram generation, and CCNN can ensure the high precision of holograms. By combining the advantages of these two methods, our proposed scheme can achieve a balance between CGH calculation speed and precision. Our proposed scheme is verified by simulated and experimental results.

This work proposes a novel approach to beam shaping and quality enhancement by employing a single-chip metasurface instead of a complex lens system. The designed self-collimating laser source, with a total size of less than 10 mm, demonstrates strong potential for integration into compact LiDAR systems.

The combination of quantitative phase microscopy (QPM) with imaging flow cytometry (IFC) enables label-free and multi-parameter single-cell analysis. Here, we present a simple yet powerful QPM-IFC platform, the spatial microfluidic holographic integrated (SMHI) platform, which uniquely integrates spatial hydrodynamic focusing microfluidics with digital holographic microscopy (DHM) to achieve high-fidelity single-cell QPM reconstruction without digital refocusing in 0.34 seconds, accounting for only 4.41% of the typical process ( ~ 7.71 seconds). We develop a high-dimensional phase feature hierarchy and implement a maximun-relevance and minimun-redundancy incremental feature selection (MRMR-IFS) strategy, which effectively addresses feature redundancy and constructs the optimal feature set. Consequently, a prediction accuracy of >99.9% is achieved across multiple cancer cell types, breast cancer subtypes, and blood cells, demonstrating its efficacy in analyzing highly heterogeneous cell populations. Notably, this system also exhibits high accuracy in analyzing simulated blood samples, highlighting its great potential in practical applications.

The resolution of lensless on-chip microscopy is mainly limited by the pixel size of the image sensor. Many superresolution techniques have emerged to solve the problem of insufficient imaging resolution. Most existing pixel super-resolution technologies rely on precise electric translation stage for hundreds of high-precision displacements, or on expensive tunable lasers to generate diffraction diversity. Conventional ptychography iterative engine (PIE) is considered an effective method for improving imaging resolution, but it is prone to oscillations in the early stage of iteration. In this paper, we propose a ptychography imaging technique based on scattering multiplexing, which involves coating the surface of the image sensor with a layer of polystyrene microspheres, and utilizing a 4 x 3 LED array to sequentially illuminate the sample. An innovative ptychography reconstruction algorithm based on dual amplitude gradient descent (DAGD) is designed for the reconstruction from the holograms, which effectively avoids the problems of slow convergence speed and obvious oscillation. Compared with other similar technologies, our system has no moving parts and uses inexpensive partially coherent light illumination. It only records 12 holograms and reaches the imaging resolution of 1.23 mu m, which is 1.36 times pixel super-resolution compared with the pixel size of the sensor.

Applications such as mobile imaging, industrial testing, and biomedicine drive the demand for cost-effective and portable imaging systems. Lensless imaging offers advantages such as compact size and low cost, due to its independence from traditional imaging lenses. In this paper, building on Fresnel zone aperture (FZA) technology, we propose a dual compound FZA (DCFZA) for lensless imaging. The design incorporates the twin-image elimination concept from co-axial holography and optimizes the phase combinations of the constituent FZAs, each operating at different orders, to minimize reconstruction noise. Moreover, a high-order back-propagation (HBP) reconstruction method is employed to overcome the information throughput limitation imposed by the minimum aperture size, enabling rapid and sample-independent reconstruction. The DCFZA-based lensless imaging system achieves an 2-fold improvement in resolution over the FZA-based system with the same minimum aperture and a higher enhancement in reconstruction quality compared to BP reconstruction. While maintaining comparable imaging quality, it significantly reduces reconstruction time compared to traditional compressive sensing (CS) based algorithms. Experimental results demonstrate its capability for high-throughput imaging, edge-enhanced imaging, and text recognition. Relying on its rapid and high-throughput reconstruction algorithm, which utilizes direct backpropagation (BP) without iteration or sample-dependent training models, this technology has the potential to achieve real-time imaging with low cost and high compactness system.

Droplet microfluidic chips have emerged as an efficient platform for single-cell analysis due to their high sensitivity, efficiency, and throughput, showing significant potential in pathogen detection. However, current droplet microfluidic chips encounter challenges in large-scale droplet quantification and precise imaging, rendering them unsuitable for the high-throughput pathogen detection required for a large number of samples. To address these issues, this study developed a high-precision fluorescence imaging system utilizing a confocal reflective fluorescence approach, which is an advanced microscopy technique that combines confocal microscopy and reflected fluorescence imaging. It can obtain fluorescence signal and reflected light signal in the sample at the same time, so as to provide richer and more comprehensive image information. The system offers a large field of view (17.8 mm x 17.8 mm) and high resolution (20 mu m), enabling the rapid imaging of 30,000 droplets within 10 s, thereby significantly enhancing detection efficiency and automation. Additionally, the enzymatic reaction of Escherichia coli (E. coli) was implemented using the droplet microfluidic chip to validate the effectiveness of the optical imaging system, with results demonstrating the system's capability to accurately capture fluorescence changes during the reaction.

Obtaining the ground truth for imaging through the scattering objects is always a challenging task. Furthermore, the scattering process caused by complex media is too intricate to be accurately modeled by either traditional physical models or neural networks. To address this issue, we present a learning from better simulation (LBS) method. Utilizing the physical information from a single experimentally captured image through an optimization-based approach, the LBS method bypasses the multiple-scattering process and directly creates highly realistic synthetic data. The data can then be used to train downstream models. As a proof of concept, we train a simple U-Net solely on the synthetic data and demonstrate that it generalizes well to experimental data without requiring any manual labeling. 3D holographic particle field monitoring is chosen as the testing bed, and simulation and experimental results are presented to demonstrate the effectiveness and robustness of the proposed technique for imaging of complex scattering media. The proposed method lays the groundwork for reliable particle field imaging in high concentration. The concept of utilizing realistic synthetic data for training can be significantly beneficial in various deep learning-based imaging tasks, especially those involving complex scattering media.

As an important branch of free-space optical (FSO) communication technology, ultraviolet (UV) communication is mainly applied to mobile communication platforms represented by unmanned aerial vehicle (UAV). With the development of LEDs and UV detector devices, UAV UV communication technology has shown great potential in related fields. But at the same time, it also faces some challenges. As the communication distance increases, the path loss of the UV communication system can reach 0.12dB/m, and the variation in bit error rate (BER) can rapidly deteriorate from the order of 10(-8) to 10(-1). Additionally, the UV communication system mounted on a UAV platform can emit radiation into the environment, which may have negative effects on human health when the radiation intensity exceeds 0.5 mu W/cm(2). These issues can be summarized as the availability, stability, and effectiveness of UAV-based UV communication technology. This paper aims to comprehensively address both UAVs and UV communication, providing a detailed introduction to the challenges and solutions facing UAV-based UV communication technology. Focusing on the specific aspects of these three issues, the paper first introduces the research background, value, and challenges of UAV-based UV communication technology, and investigates the current research status of UV communication channel models and positioning techniques. In order to solve the problem of UV environmental radiation, the article goes on to introduce beamforming and power control in UV optical communication technology. To solve the problem of reducing signal attenuation and increasing the communication range, the article introduces diversity technology and networking technology. In order to balance the communication quality and communication rate during UAV movement, the article introduces adaptive modulation and adaptive coding technology. Finally, the future development direction of UAV UV communication technology is summarised.

We demonstrate an all-fiber oscillator with high optical-to-optical efficiency using laser diodes working at 915nm as the pump sources to reduce the demand for thermal management. When the output power of the bidirectional pumped oscillator is 1.16kW, the optical-to-optical efficiency is as high as 75.4%. In this working state, the output characteristics of the oscillator are observed. The Raman suppression ratio is 41.23dB and the beam quality factor Mx2 = 1.14, My2 = 1.29. Analyzing the time trajectory of the highest power output, there are no significant characteristic peaks in the time domain signal and the frequency domain signal, which indicates that the oscillator has good stability.Improving the output characteristics of the 915nm pumping lasers has a positive significance for the application of non-strict ambient temperature control.

In digital holographic particle field imaging,the small aperture angle of particle diffraction results in an increased depth of focus during reconstruction.This leads to a significantly lower axial positioning accuracy compared with lateral positioning accuracy.Therefore,higher axial positioning accuracy can be achieved by increasing the illumination wavelength,which is equivalent to increasing the particle aperture angle.This study proposes the use of infrared coherent light source to illuminate the particle field to improve the axial positioning accuracy of digital holographic particle field reconstruction without increasing the complexity of algorithms and systems.This study theoretically analyzes the relationship between focal depth and axial positioning accuracy in digital holographic particle field reconstruction.Simulation and analysis of holographic particle field reconstruction are conducted under green,red,and infrared light illumination.Moreover,holographic imaging experiments of polystyrene microsphere particle field based on these three light sources are performed.The simulation and experimental results show that compared with red and green lights,the infrared light source reduces the focal depth by approximately 19%and 39%,respectively.Also,increasing the wavelength weakens the interlayer interference of defocused images,thereby improving axial positioning accuracy.

Unmanned aerial vehicle (UAV) ultraviolet (UV) communication has garnered significant attention due to its broad range of applications. Currently, there are few decoding algorithms specifically tailored for UAV UV multiple-input-single-output (MISO) communication scenarios. We propose an enhanced maximum a posteriori (MAP) and log-likelihood ratio belief propagation (LLRBP) algorithm for MISO UV communication systems. This algorithm strategically ranks interference signals based on the signal-to-interference plus noise ratio (SINR), effectively eliminates errors in transmitter information recovery, and is named the SINR-MAP-LLRBP Algorithm. Building upon this algorithm, we establish the optimal quantitative relationship for the signal-to-noise ratio (SNR) in the MISO UVC system. Additionally, we introduce an enhanced decoding algorithm for MISO UV communication systems, leveraging the residual-based loop iteration methodology, named as the Res-SINR-MAP-LLRBP Algorithm. This algorithm provides an additional decoding gain compared to the SINR-MAP-LLRBP Algorithm.

SignificancePathogens have caused numerous, large, and deadly outbreaks, and various disinfection techniques have been applied to stop the spread of harmful pathogens. These techniques are classified as chemical and physical disinfection based on the principle of inactivation. However, the frequently used disinfection techniques can cause indiscriminate harm to the host. Ultraviolet (UV) light is the most popular light disinfection technique, followed by chemical, thermal, ionizing radiation, and thermal disinfection, which harm the pathogen-containing biological tissues by altering their protein or nucleic acid composition. These technologies are unsuitable for prolonged disinfection in the presence of human activity. Thus, there is a need to explore a disinfection technology that can be safely used on living beings for a prolonged period. The disinfection technique must inactivate the pathogen and protect the host from the pathogen but cause no harm to the host cells. Light-based host-nondestructive disinfection techniques specifically inactivate harmful pathogens without damaging the surrounding tissues and have important applications in space disinfection, disease treatment, food preservation, and biologics production.ProgressCurrently, far-UVC, antibacterial blue light, and low-power ultrashort pulse laser are some of the most widely used nondestructive light disinfection techniques. Far-UVC damages nucleic acids by forming cyclobutane pyrimidine dimers between thymine molecules in DNA/RNA, thereby eliminating the ability of pathogens to replicate and infect. The 207-222 nm far-UVC is considered safe as the short wavelength penetrates only the stratum corneum, the skin's outermost layer, and the outer surface of the eye. However, research on far-UVC is limited because applicable dose standards and inactivation kinetics are lacking. The disinfection ability of antibacterial blue light is due to the presence of certain endogenous photosensitizers inside microorganisms that absorb light energy, such as porphyrins and flavins. These endogenous photosensitizers convert some substances into reactive oxygen species (ROS), which destroy the internal structures, such as organelles, of the microorganism by oxidizing neighboring biomolecules and subsequently inactivating the microorganism. Currently, research on antimicrobial blue light has focused on bacterial and fungal disinfection, with limited research on the inactivation of viruses and protozoans. Because viruses do not have endogenous photosensitizers, antimicrobial blue light requires exogenous photosensitizers to inactivate the virus through the ROS mechanism. Low-power ultrashort pulse laser creates vibrations on the surface of pathogens using femtosecond-level light pulses to induce protein remodeling, which destroys the surface of pathogens and prevents infection. Different proteins differ in densities and vibration durations, so a low-power ultrashort pulse laser selectively inactivates pathogens by varying the pulse frequency without causing damage to other biological tissues. As the inactivation process does not produce unknown intermediates, it is safe to use in the production of vaccines, sterilization of blood products, and disinfection of cell culture medium.Conclusions and ProspectsLight-based host-nondestructive disinfection techniques should be evaluated using the actual absorbed dose of microorganisms as the criterion for the inactivation effect, and a comprehensive light energy inactivation rate model should be established. It is necessary to explore the optimal light energy density, optimal inactivation wavelength, and multiwavelength and multimodal synergistic disinfection to promote the application of large-scale energy-efficient light inactivation technology to prevent the spread of pathogens.

Lens-free on-chip microscopy with RGB LEDs[LFOCM-RGB]provides a portable,cost-effective,and high-throughput imaging tool for resource-limited environments.However,the weak coherence of LEDs limits the high-resolution imaging,and the luminous surfaces of the LED chips on the RGB LED do not overlap,making the coherence-enhanced executions tend to undermine the portable and cost-effective implementation.Here,we propose a specially designed pinhole array to enhance coherence in a portable and cost-effective implementation.It modulates the three-color beams from the RGB LED sepa-rately so that the three-color beams effectively overlap on the sample plane while reducing the effective light-emitting area for better spatial coherence.The separate modulation of the spatial coherence allows the temporal coherence to be modu-lated separately by single spectral filters rather than by expensive triple spectral filters.Based on the pinhole array,the LFOCM-RGB simply and effectively realizes the high-resolution imaging in a portable and cost-effective implementation,offering much flexibility for various applications in resource-limited environments.

Lensless holographic projection technology allows for removing of the projection lens and simplifies the optical projection system. It has great potential to be applied in the fields of three-dimensional printing, integrated circuit fabrication, and display. In this article, for image minified lensless holographic projection based on digital micro-mirror device (DMD), we examine the aberration that arises from the use of the DMD in the presence of oblique converging spherical wave illumination. To correct this aberration, we employ a diagonal compression technique on the target pattern. Additionally, we address the issue of speckle noise by relaxing the amplitude constraint in the non-signal domain, which is generated by our proposed aberration correction method. Importantly, this relaxation is achieved without reducing the size of the valid image. Our experimental results demonstrate the successful reconstruction of high-quality images in a lensless holographic projection system.

We present a lensless on chip microscopy system based on array illumination and a super-resolution reconstruction algorithm based on sub-pixel displacement, the resolution of which breaks through the limitation of pixel size.