Large Depth Of Field Research Articles

Holographic image denoising for dense droplet field using conditional diffusion model.

The Letter delves into an approach to holographic image denoising, drawing inspiration from the generative paradigm. It introduces a conditional diffusion model framework that effectively suppresses twin-image noises and speckle noises in dense particle fields with a large depth of field (DOF). Specific training and inference configurations are meticulously outlined. For evaluation, the method is tested using calibration dot board data and droplet field data, encompassing gel atomization captured via inline holography and aviation kerosene swirl spray through off-axis holography. The performance is assessed using three distinct metrics. The metric outcomes, along with representative examples, robustly demonstrate its superior noise reduction, detail preservation, and generalization capabilities when compared to two other methods. The proposed method not only pioneers the field of generative holographic image denoising but also highlights its potential for industrial applications, given its reduced dependency on high-quality training labels.

Terahertz super-resolution imaging with large depth of field enabled by the terajet of bilayer dielectric spheres

Terahertz imaging has found extensive applications in non-destructive testing, security inspection, and other various fields. Intensive research on terahertz imaging systems has been executed to pursue high performance on imaging resolution and depth of field (DOF). However, the terahertz imaging systems with both high imaging resolution and large DOF have rarely been reported. In this paper, a mesoscopic-sized dielectric bilayer sphere-assisted super-resolution imaging method was proposed to simultaneously achieve enormously improved imaging resolution and extended DOF. Simulation analyses revealed that the ultrathin and long terajets were generated by the well-designed bilayer sphere. The THz super-resolution image for the samples can be captured by the point-by-point terajet scanning. The experimental results demonstrated that the best resolution reached up to 0.4λ, the DOF with super-resolution was up to 2λ, and the DOF with sub-wavelength resolution was up to 4λ. This method holds great potential for widespread application in terahertz imaging and detection, especially for curved or complex sample structures.

High-visibility calculating ghost imaging with self-reconstruction capability and extendible depth-of-field

We customized light speckle fields with both super-bunching and non-diffracting properties, accordingly named as the super-bunching, non-diffracting (SP-ND) speckle fields, by introducing pupil function of a ring aperture with azimuthally correlated phases in the vertically opposite angles. Calculating ghost imaging based on the SP-ND speckle fields was demonstrated to be of higher visibility, higher spatial resolution and larger depth of field than that based on the conventional speckle fields such as pseudo-thermal fields. Interestingly, the SP-ND speckles are also of self-healing capability in respect of not only the speckle intensity distribution but also the high-order coherence properties. Therefore, even when the SP-ND speckle fields are seriously disturbed, for example, blocked partially by an opaque obstacle, ghost images are able to be reconstructed once the object is placed in the self-healed speckle fields.

Projection superimposition for the generation of high-resolution digital grating.

Fringe projection profilometry based on MEMS micro-vibration mirrors is very promising due to its rapid projection, large depth of field, compact size, and low cost. Although high-frequency fringes can achieve accurate reconstruction, the projector must offer sufficient pixel resolution. In this paper, we proposed a high-resolution projection technique called the delay superposition method. During a single exposure time of the camera, the projector projects a group of low-resolution fringe patterns, which are delayed according to the movement characteristics of the vibration mirror. Then, the camera exposure superimposes these low-resolution images to form a high-resolution image. These two steps effectively subdivide the angle intervals, thereby achieving a pixel interpolation. Finally, experimental results show that the proposed method can significantly improve the projector's pixel resolution and reconstruction accuracy. The proposed method allows the MEMS projector's pixel resolution (along one direction) to far exceed that of common DLP projectors. It holds great application potential for high-frequency fringe projection.

A foreground-context dual-guided network for light-field salient object detection

Light-field salient object detection (SOD) has become an emerging trend as it records comprehensive information about natural scenes that can benefit salient object detection in various ways. However, salient object detection models with light-field data as input have not been thoroughly explored. The existing methods cannot effectively suppress the noise, and it is difficult to distinguish the foreground and background under challenging conditions including self-similarity, complex backgrounds, large depth of field, and non-Lambertian scenarios. In order to extract the feature of light-field images effectively and suppress the noise in light-field, in this paper, we propose a foreground and context dual guided network. Specifically, we design a global context extraction module (GCEM) and a local foreground extraction module (LFEM). GCEM is used to suppress global noise and roughly predict saliency maps. GCEM also can extract global context information from deep-level features to guide decoding process. By extracting local information from shallow-level, LFEM refines the prediction obtained by GCEM. In addition, we use RGB images to enhance the light-field images before the input GCEM. Experimental results show that our proposed method is effective in suppressing global noise and achieves better results when dealing with transparent objects and complex backgrounds. The experimental results show that the proposed method outperforms several other state-of-the-art methods on three light-field datasets.

Fast Three-Dimensional Profilometry with Large Depth of Field.

By applying a high projection rate, the binary defocusing technique can dramatically increase 3D imaging speed. However, existing methods are sensitive to the varied defocusing degree, and have limited depth of field (DoF). To this end, a time-domain Gaussian fitting method is proposed in this paper. The concept of a time-domain Gaussian curve is firstly put forward, and the procedure of determining projector coordinates with a time-domain Gaussian curve is illustrated in detail. The neural network technique is applied to rapidly compute peak positions of time-domain Gaussian curves. Relying on the computing power of the neural network, the proposed method can reduce the computing time greatly. The binary defocusing technique can be combined with the neural network, and fast 3D profilometry with a large depth of field is achieved. Moreover, because the time-domain Gaussian curve is extracted from individual image pixel, it will not deform according to a complex surface, so the proposed method is also suitable for measuring a complex surface. It is demonstrated by the experiment results that our proposed method can extends the system DoF by five times, and both the data acquisition time and computing time can be reduced to less than 35 ms.

The simulation and application of large depth of field 3D points reconstruction based on PSF and MOGA improved network

In this paper, an acronym for stereo vision calibration-point spread function (SVC-PSF), is designed to accurately represent image degradation in large-depth-of-field (LDOF). The error of the dataset generated by the SVC-PSF is 50 % lower than that of Stereo Vision Calibration (SVC) method in LDOF. A three-dimensional point coordinate reconstruction artificial neural network (ANN) is improved through a multi-objective genetic algorithm (MOGA) to satisfy both computational accuracy and real-time performance. The MOGA improved ANN can reconstruct 1250 points within 0.069 s on an embedded platform. Finally, SVC-PSF is used to generate a dataset, and the MOGA-ANN improved model is trained based on this dataset. An embedded device, stereo displacement measurement device (SDMD) is designed to run SVC-PSF-MOGA-ANN algorithm, and this device is applied in thermal power plant boiler expansion monitor. The real-time monitor results shows that when the boiler power increases, more working medium is needed to produce steam, so the boiler's own weight increases, and the boiler expands downward under the action of gravity. At low power, the required working medium is reduced, and the corresponding expansion amount is also reduced.

Aberration correction for deformable-mirror-based remote focusing enables high-accuracy whole-cell super-resolution imaging

Single-molecule localization microscopy (SMLM) enables three-dimensional (3D) investigation of nanoscale structures in biological samples, offering unique insights into their organization. However, traditional 3D super-resolution microscopy using high numerical aperture (NA) objectives is limited by imaging depth of field (DOF), restricting their practical application to relatively thin biological samples. Here, we developed a unified solution for thick sample super-resolution imaging using a deformable mirror (DM) which served for fast remote focusing, optimized point spread function (PSF) engineering, and accurate aberration correction. By effectively correcting the system aberrations introduced during remote focusing and sample aberrations at different imaging depths, we achieved high-accuracy, large DOF imaging ( ∼8 μm ) of the whole-cell organelles [i.e., nuclear pore complex (NPC), microtubules, and mitochondria] with a nearly uniform resolution of approximately 35 nm across the entire cellular volume.

Phase Error Reduction for a Structured-Light 3D System Based on a Texture-Modulated Reprojection Method.

Fringe projection profilometry (FPP), with benefits such as high precision and a large depth of field, is a popular 3D optical measurement method widely used in precision reconstruction scenarios. However, the pixel brightness at reflective edges does not satisfy the conditions of the ideal pixel-wise phase-shifting model due to the influence of scene texture and system defocus, resulting in severe phase errors. To address this problem, we theoretically analyze the non-pixel-wise phase propagation model for texture edges and propose a reprojection strategy based on scene texture modulation. The strategy first obtains the reprojection weight mask by projecting typical FPP patterns and calculating the scene texture reflection ratio, then reprojects stripe patterns modulated by the weight mask to eliminate texture edge effects, and finally fuses coarse and refined phase maps to generate an accurate phase map. We validated the proposed method on various texture scenes, including a smooth plane, depth surface, and curved surface. Experimental results show that the root mean square error (RMSE) of the phase at the texture edge decreased by 53.32%, proving the effectiveness of the reprojection strategy in eliminating depth errors at texture edges.

Design of an Achromatic Graphene Oxide Metalens with Multi-Wavelength for Visible Light

Dispersion control is a critical aspect in nano-optical systems. Moreover, chromatic aberration significantly impacts image quality. Despite metasurfaces being a novel approach to tackle chromatic aberration in diffractive lenses, numerous challenges hinder their practical implementation due to the complexity of 3D fabrication techniques and high manufacturing costs. In contrast, ultra-thin graphene oxide lenses are simpler and less expensive to manufacture. The optical performance of graphene oxide lenses, such as high focusing efficiency, large depth of field, wide bandwidth, and zooming capability, depends on the design of the positional arrangement of reduced graphene oxide regions. In this study, we utilized the self-constructed datasets to train machine learning models based on the structure of the graphene oxide lens and combined it with intelligent optimization algorithms. This approach facilitated the design of the graphene oxide achromatic lens in multi-wavelengths with high-performance. Experimental results substantiate that the designed ultra-thin graphene oxide lens, with a thickness of ~200 nm, effectively controls dispersion across multiple incident wavelengths (450, 550, and 650 nm) and achieves super resolution with consistent intensity at the focal point. Our graphene oxide lens holds the potential for integration into micro-optical systems that demand dispersion control, providing broad applications in optical imaging, optical communication, the biomedical field, and beyond.

Thin and Large Depth-Of-Field Compound-Eye Imaging for Close-Up Photography

Large depth of field (DOF) and stereo photography are challenging yet rewarding areas of research in close-up photography. In this study, a compound-eye imaging system based on a discrete microlens array (MLA) was implemented for close-range thin imaging. A compact imaging system with a total length of 3.5 mm and a DOF of 7 mm was realized using two planar aspherical MLAs in a hexagonal arrangement. A new three-layer structure and discrete arrangement of sublenses were proposed to suppress stray light and enable the spatial refocusing method, which restores image information at different object depths. The system is successfully fabricated, and the system performance is carefully investigated. Our system offers a large depth of field, high resolution, and portability, making it ideal for close-up photography applications requiring a short conjugate distance and small device volume, while also addressing the issue of crosstalk between adjacent channels.

Dual-mode optical see-through integral imaging 3D display with large depth of field

Conventional optical see-through integral imaging display system using holographic optical element (HOE) is actually in focused display mode whose central depth plane (CDP) is very close to the HOE plane, and the depth of field (DOF) of the system is limited to several centimeters around the HOE plane. In this paper, we proposed a dual-mode optical see-through integral imaging display system to reconstruct 3D images in both real and virtual display modes simultaneously and greatly extend the DOF. A dual-mode lens array HOE (DM-LAHOE) was specially designed by using two sets of lens arrays, and it featured as a lens array with positive focal length and negative focal length under the illumination of different probe beams. The designed DM-LAHOE can generate two CDPs away from the HOE plane. Hence, the real mode and virtual mode of the integral imaging display can be achieved simultaneously, and the DOF of the system was greatly improved by increasing the focal length of the recorded lens array and the number of CDPs. In the experiment, the DM-LAHOE with large focal lengths of ±14mm was fabricated, and the DOF of the proposed system was increased from 24.5mm to 190mm. The proposed dual-mode optical see-through integral imaging display system is expected to be applied in the fields of augmented reality display, vehicle display, window display, etc.

Characterization and chronology of charcoal found in the volcanic ashfall that impacted a late Valdivia community in coastal Ecuador

Several samples of fossilized wood (charcoal) were collected in the Papayita archaeological site, in coastal Ecuador. This carbonized material was encountered inside a layer of volcanic ash that sealed the site. The ash-sized tephra was produced by a sub-Plinian eruption from the Guagua Pichincha volcano contemporaneous with the late Valdivia phases during the Formative Period. Each of the samples was sectioned into 10 to 15 subsamples and examined under a Scanning Electron Microscope (SEM), producing high-resolution images with a large depth of field where the anatomical structures and their geochemical composition were vividly discernible. Each sample corresponds to organic matter of vegetable origin, that is, carbonized wood in the form of small rocks, whose appearance is that of carbonized woody tree trunks and or branches. We were able to observe vascular structures, specifically bundles of xylem. It was possible to conclude that these tracheids underwent a physicochemical transformation typical of petrification processes, leaving the molds intact. This allowed us to determine structural elements that support the identification of the group of plants to which these samples belong, through the methodology of comparison of the anatomical components of current species. The fossilized wood structures are three-dimensional and present characteristics that correspond to the group of higher plants, Gymnosperms, of the Podocarpaceae group. Among them, quadrangular tracheids, circular hole-shaped pits in the vascular system, and absent resin canals stand out. Central to the analysis is the presence of transverse parenchyma, which can be ascertained to correspond to vegetation from climates that are temperate or cold.

Extended depth-of-field resolution enhancement microscopy imaging for neutralizing the impact of mineral inhomogeneous surface

<p>One of the most fundamental experimental methods in geoscience is to observe minerals under high magnification objectives. However, uneven microsurfaces in thin sections occur due to the irregular constituent distribution and varying hardness of minerals in natural rocks. Consequently, the conflict between large depth-of-field (DOF) and high-resolution in microscopy imaging leads to random out-of-focus issues when observing thin sections with high resolution microscopy. Although existing super-resolution algorithms promise to improve visual performance, reconstructing images with both large DOF and high-resolution simultaneously remains challenging. We address this problem by guiding the networks with optical information. Utilizing DOF information from low-resolution data, we propose an optically induced generative adversarial network (OIGAN) to neutralize the impact through computational imaging. In OIGAN, optical DOF information from low-resolution data facilitates to achieve spatial-adaptive extended-DOF resolution enhancement imaging, without incorporating extended DOF high-resolution data for supervision. The approach, trained and evaluated on the dataset with 233,156 images (115,346 pairs of low- and high-resolution data), outperforms four comparison methods on various minerals and optical conditions, leading to at least 1.54dB increase on peak signal-to-noise ratio (PSNR). Specifically, OIGAN significantly improves the accuracy of fluid inclusion ice-melting temperature measurement, reducing mean error by 65%, and enhances mineral classification accuracy with 1.5%~15% increase. OIGAN offers an insight of integrating physical knowledge into neural networks, facilitating self-identification of minerals, automatic microthermometry of fluid inclusions and other geoscience tasks via microscopy.</p>

Multifocus double-helix point spread function microscopy for 3D single particle tracking

Double-helix point spread function (DH-PSF) microscopy can realize three-dimensional single particle tracking (3D SPT) on a nanoscale, and is widely used in life sciences and other fields. However, its imaging depth-of-field (DOF) and localization accuracy are limited, which hinders its application in thick samples in vivo. To address this issue, this paper proposes a z-splitter prism-based multifocus DH-PSF microscopy (ZPMDM) method and system to improve the DOF and localization accuracy of DH-PSF microscopy without scanning. It solves the problem of large DOF detection of 3D SPT in whole living cells. By means of systematic calibration, the average 3D localization accuracies of three channels of ZPMDM are determined to be <i>σ</i><sub>L(<i>x, y, z</i>)</sub> = (4.4 nm, 4.6 nm, 10.5 nm), <i>σ</i><sub>M(<i>x, y, z</i>)</sub> = (4.3 nm, 4.2 nm, 8.2 nm), and <i>σ</i><sub>R(<i>x, y, z</i>)</sub> = (4.8 nm, 4.4 nm, 10.3 nm). And the effective DOF of the system is extended to 6 μm. Furthermore, the ZPMDM system is used to track fluorescent microspheres in a glycerol-water mixture across a large depth-of-field range. The Brownian motion of the fluorescent microspheres in the mixture solution is also investigated. The experimental results demonstrate that the errors between the experimentally obtained diffusion coefficients and the theoretically calculated diffusion coefficients are all within 10%. The reliability of the ZPMDM system in achieving single-particle 3D tracking imaging is verified in this study. The validity of the method is further verified by preliminarily investigating the phagocytosis phenomenon of live macrophages. It is of significance for the development and application of nanoscale 3D SPT. The ZPMDM system is shown in the attached figure.

Rapid three-dimensional detection of harmful algae using holographic microimaging

Holographic microimaging technology is widely used in the plankton detection due to its large depth of field and ability to record three-dimensional (3D) information. However, traditional methods involve tedious and time-consuming steps, e.g., region of interest (ROI) extraction, dense reconstruction, focused evaluation, and focused ROI recognition. As a result, traditional methods are difficult to detect in real time. In this paper, we propose a neural network-based 3D detection method, which for the first time uses the holograms to obtain 3D sampling data for algae detection, and innovatively designs a convolutional neural network (DHCNN) to improve the speed and accuracy of detection. A dataset containing 3D samples of eight harmful algae species is established, and the trained DHCNN model achieves a mean average precision of 99.2 % on the test set. We find that accuracy is higher with 3D sampled data, which contains more valid information than with autofocused ROIs. Moreover, detection rate is 99.5 %, which means it could detect attached algae and algae abundance statistics are more accurate. And detection time of the proposed method is only 0.066 s, which is 20 times faster than the traditional method. We believe that the proposed method represents an effective approach for real-time algae monitoring.

Electrically tunable lens assisted absolute phase unwrapping for large depth-of-field 3D microscopic structured-light imaging

This paper presents an absolute phase unwrapping method for large depth-of-field (DOF) microscopic structured-light 3D imaging. We calculate the wrapped phase maps and the fringe contrast maps from fringe images captured under various focus settings realized by an electrically tunable lens. From the fringe contrast maps, we extract each in-focus pixel and determine its approximate depth value from the focus settings. The approximate depth map is then used to create an artificial phase map. The geometric-constraint-based phase unwrapping method is adopted to unwrap the phase of all in-focus pixels using the artificial phase map. The unwrapped in-focus phase map is then used to reconstruct 3D coordinates for the entire DOF with the multi-focus pin-hole model. Experimental results demonstrate that our proposed method only requires three phase-shifted fringe patterns to achieve large DOF 3D imaging.

Hierarchical deconvolution dehazing method based on transmission map segmentation.

Images captured in fog are often affected by scattering. Due to the absorption and scattering of light by aerosols and water droplets, the image quality will be seriously degraded. The specific manifests are brightness decrease, contrast decrease, image blur, and noise increase. In the single-image dehazing method, the image degradation model is essential. In this paper, an effective image degradation model is proposed, in which the hierarchical deconvolution strategy based on transmission map segmentation can effectively improve the accuracy of image restoration. Specifically, the transmission map is obtained by using the dark channel prior (DCP) method, then the transmission histogram is fitted. The next step is to divide the image region according to the fitting results. Furthermore, to more accurately recover images of complex objects with a large depth of field, different levels of inverse convolution are adopted for different regions. Finally, the sub-images of different regions are fused to get the dehazing image. We tested the proposed method using synthetic fog images and natural fog images respectively. The proposed method is compared with eight advanced image dehazing methods on quantitative rating indexes such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM), image entropy, natural image quality evaluator (NIQE), and blind/referenceless image spatial quality evaluator (BRISQUE). Both subjective and objective evaluations show that the proposed method achieves competitive results.

High-brightness hybrid compressive light field display with improved image quality.

Previous LCD-based multiplicative compressive light field (CLF) display has the trade-off between the brightness and the depth of field (DOF). In this paper, we propose a hybrid CLF display using a reflective polarizer and RGB mini-LED panel. By the polarization-multiplexing and the reflector dam (RD) designed on the mini-LED panel, the proposed system can preserve high brightness while enhancing the DOF. Then, a decomposition algorithm is proposed to improve the image quality by depth segmentation and limiting the motion parallax. Compared to the conventional hybrid CLF display, the brightness of the proposed system reaches 348 nits and the reconstruction quality achieves structural similarity index measure (SSIM) improvement by 0.12. The experiments also demonstrate that the proposed method could achieve a higher brightness, larger depth of field, and higher image quality.

E2E-BPF microscope: extended depth-of-field microscopy using learning-based implementation of binary phase filter and image deconvolution

Several image-based biomedical diagnoses require high-resolution imaging capabilities at large spatial scales. However, conventional microscopes exhibit an inherent trade-off between depth-of-field (DoF) and spatial resolution, and thus require objects to be refocused at each lateral location, which is time consuming. Here, we present a computational imaging platform, termed E2E-BPF microscope, which enables large-area, high-resolution imaging of large-scale objects without serial refocusing. This method involves a physics-incorporated, deep-learned design of binary phase filter (BPF) and jointly optimized deconvolution neural network, which altogether produces high-resolution, high-contrast images over extended depth ranges. We demonstrate the method through numerical simulations and experiments with fluorescently labeled beads, cells and tissue section, and present high-resolution imaging capability over a 15.5-fold larger DoF than the conventional microscope. Our method provides highly effective and scalable strategy for DoF-extended optical imaging system, and is expected to find numerous applications in rapid image-based diagnosis, optical vision, and metrology.