Showing papers on "Depth of field published in 2018"

End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging

Abstract: In typical cameras the optical system is designed first; once it is fixed, the parameters in the image processing algorithm are tuned to get good image reproduction. In contrast to this sequential design approach, we consider joint optimization of an optical system (for example, the physical shape of the lens) together with the parameters of the reconstruction algorithm. We build a fully-differentiable simulation model that maps the true source image to the reconstructed one. The model includes diffractive light propagation, depth and wavelength-dependent effects, noise and nonlinearities, and the image post-processing. We jointly optimize the optical parameters and the image processing algorithm parameters so as to minimize the deviation between the true and reconstructed image, over a large set of images. We implement our joint optimization method using autodifferentiation to efficiently compute parameter gradients in a stochastic optimization algorithm. We demonstrate the efficacy of this approach by applying it to achromatic extended depth of field and snapshot super-resolution imaging.

Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery

Abstract: Holography encodes the three-dimensional (3D) information of a sample in the form of an intensity-only recording. However, to decode the original sample image from its hologram(s), autofocusing and phase recovery are needed, which are in general cumbersome and time-consuming to perform digitally. Here we demonstrate a convolutional neural network (CNN)-based approach that simultaneously performs autofocusing and phase recovery to significantly extend the depth of field (DOF) and the reconstruction speed in holographic imaging. For this, a CNN is trained by using pairs of randomly defocused back-propagated holograms and their corresponding in-focus phase-recovered images. After this training phase, the CNN takes a single back-propagated hologram of a 3D sample as input to rapidly achieve phase recovery and reconstruct an in-focus image of the sample over a significantly extended DOF. This deep-learning-based DOF extension method is non-iterative and significantly improves the algorithm time complexity of holographic image reconstruction from O(nm) to O(1), where n refers to the number of individual object points or particles within the sample volume, and m represents the focusing search space within which each object point or particle needs to be individually focused. These results highlight some of the unique opportunities created by data-enabled statistical image reconstruction methods powered by machine learning, and we believe that the presented approach can be broadly applicable to computationally extend the DOF of other imaging modalities.

Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery.

Abstract: Holography encodes the three dimensional (3D) information of a sample in the form of an intensity-only recording. However, to decode the original sample image from its hologram(s), auto-focusing and phase-recovery are needed, which are in general cumbersome and time-consuming to digitally perform. Here we demonstrate a convolutional neural network (CNN) based approach that simultaneously performs auto-focusing and phase-recovery to significantly extend the depth-of-field (DOF) in holographic image reconstruction. For this, a CNN is trained by using pairs of randomly de-focused back-propagated holograms and their corresponding in-focus phase-recovered images. After this training phase, the CNN takes a single back-propagated hologram of a 3D sample as input to rapidly achieve phase-recovery and reconstruct an in focus image of the sample over a significantly extended DOF. This deep learning based DOF extension method is non-iterative, and significantly improves the algorithm time-complexity of holographic image reconstruction from O(nm) to O(1), where n refers to the number of individual object points or particles within the sample volume, and m represents the focusing search space within which each object point or particle needs to be individually focused. These results highlight some of the unique opportunities created by data-enabled statistical image reconstruction methods powered by machine learning, and we believe that the presented approach can be broadly applicable to computationally extend the DOF of other imaging modalities.

Synthetic Depth-of-Field with a Single-Camera Mobile Phone

Abstract: Shallow depth-of-field is commonly used by photographers to isolate a subject from a distracting background. However, standard cell phone cameras cannot produce such images optically, as their short focal lengths and small apertures capture nearly all-in-focus images. We present a system to computationally synthesize shallow depth-of-field images with a single mobile camera and a single button press. If the image is of a person, we use a person segmentation network to separate the person and their accessories from the background. If available, we also use dense dual-pixel auto-focus hardware, effectively a 2-sample light field with an approximately 1 millimeter baseline, to compute a dense depth map. These two signals are combined and used to render a defocused image. Our system can process a 5.4 megapixel image in 4 seconds on a mobile phone, is fully automatic, and is robust enough to be used by non-experts. The modular nature of our system allows it to degrade naturally in the absence of a dual-pixel sensor or a human subject.

Synthetic depth-of-field with a single-camera mobile phone

Abstract: Shallow depth-of-field is commonly used by photographers to isolate a subject from a distracting background. However, standard cell phone cameras cannot produce such images optically, as their short focal lengths and small apertures capture nearly all-in-focus images. We present a system to computationally synthesize shallow depth-of-field images with a single mobile camera and a single button press. If the image is of a person, we use a person segmentation network to separate the person and their accessories from the background. If available, we also use dense dual-pixel auto-focus hardware, effectively a 2-sample light field with an approximately 1 millimeter baseline, to compute a dense depth map. These two signals are combined and used to render a defocused image. Our system can process a 5.4 megapixel image in 4 seconds on a mobile phone, is fully automatic, and is robust enough to be used by non-experts. The modular nature of our system allows it to degrade naturally in the absence of a dual-pixel sensor or a human subject.

Optical see-through holographic near-eye-display with eyebox steering and depth of field control

Abstract: We propose an optical see-through holographic near-eye-display that can control the depth of field of individual virtual three-dimensional image and replicate the eyebox with dynamic steering. For optical see-through capability and eyebox duplication, a holographic optical element is used as an optical combiner where it functions as multiplexed tilted concave mirrors forming multiple copies of the eyebox. For depth of field control and eyebox steering, computer generated holograms of three-dimensional objects are synthesized with different ranges of angular spectrum. In optical experiment, it has been confirmed that the proposed system can present always-focused images with large depth of field and three-dimensional images at different distances with shallow depth of field at the same time without any time-multiplexing.

Learned phase coded aperture for the benefit of depth of field extension.

Abstract: Modern consumer electronics market dictates the need for small-scale and high-performance cameras. Such designs involve trade-offs between various system parameters. In such trade-offs, Depth Of Field (DOF) is a significant issue very often. We propose a computational imaging-based technique to overcome DOF limitations. Our approach is based on the synergy between a simple phase aperture coding element and a convolutional neural network (CNN). The phase element, designed for DOF extension using color diversity in the imaging system response, causes chromatic variations by creating a different defocus blur for each color channel of the image. The phase-mask is designed such that the CNN model is able to restore from the coded image an all-in-focus image easily. This is achieved by using a joint end-to-end training of both the phase element and the CNN parameters using backpropagation. The proposed approach provides superior performance to other methods in simulations as well as in real-world scenes.

Fully Convolutional Network-Based Multifocus Image Fusion

Abstract: As the optical lenses for cameras always have limited depth of field, the captured images with the same scene are not all in focus. Multifocus image fusion is an efficient technology that can synthesize an all-in-focus image using several partially focused images. Previous methods have accomplished the fusion task in spatial or transform domains. However, fusion rules are always a problem in most methods. In this letter, from the aspect of focus region detection, we propose a novel multifocus image fusion method based on a fully convolutional network (FCN) learned from synthesized multifocus images. The primary novelty of this method is that the pixel-wise focus regions are detected through a learning FCN, and the entire image, not just the image patches, are exploited to train the FCN. First, we synthesize 4500 pairs of multifocus images by repeatedly using a gaussian filter for each image from PASCAL VOC 2012, to train the FCN. After that, a pair of source images is fed into the trained FCN, and two score maps indicating the focus property are generated. Next, an inversed score map is averaged with another score map to produce an aggregative score map, which take full advantage of focus probabilities in two score maps. We implement the fully connected conditional random field (CRF) on the aggregative score map to accomplish and refine a binary decision map for the fusion task. Finally, we exploit the weighted strategy based on the refined decision map to produce the fused image. To demonstrate the performance of the proposed method, we compare its fused results with several start-of-the-art methods not only on a gray data set but also on a color data set. Experimental results show that the proposed method can achieve superior fusion performance in both human visual quality and objective assessment.

Extended depth-resolved imaging through a thin scattering medium with PSF manipulation

Abstract: Human ability to visualize an image is usually hindered by optical scattering. Recent extensive studies have promoted imaging technique through turbid materials to a reality where color image can be restored behind scattering media in real time. The big challenge now is to recover objects in a large field of view with depth resolving ability. Based on the existing research results, we systematically study the physical relationship between speckles generated from objects at different planes. By manipulating a given single point spread function, depth-resolved imaging through a thin scattering medium can be extended beyond the original depth of field (DOF). Experimental testing of standard scattering media shows that the DOF can be extended up to 5 times and the physical mechanism is depicted. This extended DOF is benefit to 3D imaging through scattering environment, and it is expected to have important applications in science, technology, bio-medical, security and defense.

Multi-slice ptychography with large numerical aperture multilayer Laue lenses

Abstract: The highly convergent x-ray beam focused by multilayer Laue lenses with large numerical apertures is used as a three-dimensional (3D) probe to image layered structures with an axial separation larger than the depth of focus. Instead of collecting weakly scattered high-spatial-frequency signals, the depth-resolving power is provided purely by the intense central cone diverged from the focused beam. Using the multi-slice ptychography method combined with the on-the-fly scan scheme, two layers of nanoparticles separated by 10 μm are successfully reconstructed with 8.1 nm lateral resolution and with a dwell time as low as 0.05 s per scan point. This approach obtains high-resolution images with extended depth of field, which paves the way for multi-slice ptychography as a high throughput technique for high-resolution 3D imaging of thick samples.

An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging

Abstract: Digitization of natural history collections is a major challenge in archiving biodiversity. In recent years, several approaches have emerged, allowing either automated digitization, extended depth of field (EDOF) or multi-view imaging of insects. Here, we present DISC3D: a new digitization device for pinned insects and other small objects that combines all these aspects. A PC and a microcontroller board control the device. It features a sample holder on a motorized two-axis gimbal, allowing the specimens to be imaged from virtually any view. Ambient, mostly reflection-free illumination is ascertained by two LED-stripes circularly installed in two hemispherical white-coated domes (front-light and back-light). The device is equipped with an industrial camera and a compact macro lens, mounted on a motorized macro rail. EDOF images are calculated from an image stack using a novel calibrated scaling algorithm that meets the requirements of the pinhole camera model (a unique central perspective). The images can be used to generate a calibrated and real color texturized 3Dmodel by 'structure from motion' with a visibility consistent mesh generation. Such models are ideal for obtaining morphometric measurement data in 1D, 2D and 3D, thereby opening new opportunities for trait-based research in taxonomy, phylogeny, eco-physiology, and functional ecology.

DeepFocus: learned image synthesis for computational displays

Abstract: Addressing vergence-accommodation conflict in head-mounted displays (HMDs) requires resolving two interrelated problems. First, the hardware must support viewing sharp imagery over the full accommodation range of the user. Second, HMDs should accurately reproduce retinal defocus blur to correctly drive accommodation. A multitude of accommodation-supporting HMDs have been proposed, with three architectures receiving particular attention: varifocal, multifocal, and light field displays. These designs all extend depth of focus, but rely on computationally expensive rendering and optimization algorithms to reproduce accurate defocus blur (often limiting content complexity and interactive applications). To date, no unified framework has been proposed to support driving these emerging HMDs using commodity content. In this paper, we introduce DeepFocus, a generic, end-to-end convolutional neural network designed to efficiently solve the full range of computational tasks for accommodation-supporting HMDs. This network is demonstrated to accurately synthesize defocus blur, focal stacks, multilayer decompositions, and multiview imagery using only commonly available RGB-D images, enabling real-time, near-correct depictions of retinal blur with a broad set of accommodation-supporting HMDs.

DeepLens: shallow depth of field from a single image

Abstract: We aim to generate high resolution shallow depth-of-field (DoF) images from a single all-in-focus image with controllable focal distance and aperture size. To achieve this, we propose a novel neural network model comprised of a depth prediction module, a lens blur module, and a guided upsampling module. All modules are differentiable and are learned from data. To train our depth prediction module, we collect a dataset of 2462 RGB-D images captured by mobile phones with a dual-lens camera, and use existing segmentation datasets to improve border prediction. We further leverage a synthetic dataset with known depth to supervise the lens blur and guided upsampling modules. The effectiveness of our system and training strategies are verified in the experiments. Our method can generate high-quality shallow DoF images at high resolution, and produces significantly fewer artifacts than the baselines and existing solutions for single image shallow DoF synthesis. Compared with the iPhone portrait mode, which is a state-of-the-art shallow DoF solution based on a dual-lens depth camera, our method generates comparable results, while allowing for greater flexibility to choose focal points and aperture size, and is not limited to one capture setup.

DeepLens: Shallow Depth Of Field From A Single Image

Abstract: We aim to generate high resolution shallow depth-of-field (DoF) images from a single all-in-focus image with controllable focal distance and aperture size. To achieve this, we propose a novel neural network model comprised of a depth prediction module, a lens blur module, and a guided upsampling module. All modules are differentiable and are learned from data. To train our depth prediction module, we collect a dataset of 2462 RGB-D images captured by mobile phones with a dual-lens camera, and use existing segmentation datasets to improve border prediction. We further leverage a synthetic dataset with known depth to supervise the lens blur and guided upsampling modules. The effectiveness of our system and training strategies are verified in the experiments. Our method can generate high-quality shallow DoF images at high resolution, and produces significantly fewer artifacts than the baselines and existing solutions for single image shallow DoF synthesis. Compared with the iPhone portrait mode, which is a state-of-the-art shallow DoF solution based on a dual-lens depth camera, our method generates comparable results, while allowing for greater flexibility to choose focal points and aperture size, and is not limited to one capture setup.

DeepFocus: learned image synthesis for computational display

Abstract: Reproducing accurate retinal defocus blur is important to correctly drive accommodation and address vergence-accommodation conflict in head-mounted displays (HMDs). Numerous accommodation-supporting HMDs have been proposed. Three architectures have received particular attention: varifocal, multifocal, and light field displays. These designs all extend depth of focus, but rely on computationally expensive rendering and optimization algorithms to reproduce accurate retinal blur (often limiting content complexity and interactive applications). To date, no unified computational framework has been proposed to support driving these emerging HMDs using commodity content. In this paper, we introduce Deep-Focus, a generic, end-to-end trainable convolutional neural network designed to efficiently solve the full range of computational tasks for accommodation-supporting HMDs. This network is demonstrated to accurately synthesize defocus blur, focal stacks, multilayer decompositions, and multiview imagery using commonly available RGB-D images. Leveraging recent advances in GPU hardware and best practices for image synthesis networks, DeepFocus enables real-time, near-correct depictions of retinal blur with a broad set of accommodation-supporting HMDs.

Airborne Optical Sectioning

Abstract: Drones are becoming increasingly popular for remote sensing of landscapes in archeology, cultural heritage, forestry, and other disciplines. They are more efficient than airplanes for capturing small areas, of up to several hundred square meters. LiDAR (light detection and ranging) and photogrammetry have been applied together with drones to achieve 3D reconstruction. With airborne optical sectioning (AOS), we present a radically different approach that is based on an old idea: synthetic aperture imaging. Rather than measuring, computing, and rendering 3D point clouds or triangulated 3D meshes, we apply image-based rendering for 3D visualization. In contrast to photogrammetry, AOS does not suffer from inaccurate correspondence matches and long processing times. It is cheaper than LiDAR, delivers surface color information, and has the potential to achieve high sampling resolutions. AOS samples the optical signal of wide synthetic apertures (30–100 m diameter) with unstructured video images recorded from a low-cost camera drone to support optical sectioning by image integration. The wide aperture signal results in a shallow depth of field and consequently in a strong blur of out-of-focus occluders, while images of points in focus remain clearly visible. Shifting focus computationally towards the ground allows optical slicing through dense occluder structures (such as leaves, tree branches, and coniferous trees), and discovery and inspection of concealed artifacts on the surface.

Image processing apparatus, image processing method, and storage medium

Abstract: An image processing apparatus includes a memory that stores instructions and a processor that executes the instructions to cause the image processing apparatus to function as a refocused-image generating unit generating a refocused image having a focus position changed by using image data including information indicating a direction and an intensity of a light beam entered from an object, and a depth-of-field setting unit setting a depth of field of the refocused image to be used for reproduction of a moving image based on a type of reproduction mode relating to the reproduction of the moving image.

Extended depth of field imaging through multicore optical fibers.

Abstract: Compact microendoscopes use multicore optical fibers (MOFs) to visualize hard-to-reach regions of the body. These devices typically have a large numerical aperture (NA) and are fixed-focus, leading to blurry images from a shallow depth of field with little focus control. In this work, we demonstrate a method to digitally adjust the collection aperture and therefore extend the depth of field of lensless MOF imaging probes. We show that the depth of field can be more than doubled for certain spatial frequencies, and observe a resolution enhancement of up to 78% at a distance of 50μm from the MOF facet. Our technique enables imaging of complex 3D objects at a comparable working distance to lensed MOFs, but without the requirement of lenses, scan units or transmission matrix calibration. Our approach is implemented in post processing and may be used to improve contrast in any microendoscopic probe utilizing a MOF and incoherent light.

Focal stack camera in all-in-focus imaging via an electrically tunable liquid crystal lens doped with multi-walled carbon nanotubes.

Abstract: A focal stack camera, based on an electrically tunable-focusing liquid crystal (LC) lens doped with multi-walled carbon nanotubes, is proposed to generate a single all-in-focus image of a 3D scene without depth map in a relatively short time. Focal sweep strategy of the camera is devised. Both its depth of field (DOF) and focal sweep speed are analyzed and deduced. Nano doping method is adopted to improve electro-optical features of the LC lens. To efficiently produce all-in-focus image, a weighted average algorithm for all images in the focal stack is utilized. The experiments show that the result is a high contrast at sensor resolution. It is greatly potential in optical compact 3D imaging system.

Synthetic Bessel light needle for extended depth-of-field microscopy.

Abstract: An ultra-long light needle is highly desired in optical microscopy for its ability to improve the lateral resolution over a large depth of field (DOF). However, its use in image acquisition usually relies on mechanical raster scanning, which compromises between imaging speed and stability and thereby restricts imaging performance. Here, we propose a synthetic Bessel light needle (SBLN) that can be generated and scanned digitally by complex field modulation using a digital micromirror device. In particular, the SBLN achieves a 45-fold improvement in DOF over its counterpart Gaussian focus. Further, we apply the SBLN to perform motionless two-dimensional and three-dimensional microscopic imaging, achieving both improved resolution and extended DOF. Our work is expected to open up opportunities for potential biomedical applications.

Laplacian kernel splatting for efficient depth-of-field and motion blur synthesis or reconstruction

Abstract: Simulating combinations of depth-of-field and motion blur is an important factor to cinematic quality in synthetic images but can take long to compute. Splatting the point-spread function (PSF) of every pixel is general and provides high quality, but requires prohibitive compute time. We accelerate this in two steps: In a pre-process we optimize for sparse representations of the Laplacian of all possible PSFs that we call spreadlets. At runtime, spreadlets can be splat efficiently to the Laplacian of an image. Integrating this image produces the final result. Our approach scales faithfully to strong motion and large out-of-focus areas and compares favorably in speed and quality with off-line and interactive approaches. It is applicable to both synthesizing from pinhole as well as reconstructing from stochastic images, with or without layering.

Correlation plenoptic imaging with entangled photons

Abstract: Plenoptic imaging (PI) is an optical technique to perform three-dimensional imaging in a single shot. It is enabled by the simultaneous measurement of both the location and the propagation direction of light in a given scene. Despite being very useful for extending the depth of field, such technique entails a strong trade- off between spatial and angular resolution. This makes the resolution and the maximum achievable depth of focus inversely proportional; hence, resolution cannot be diffraction-limited. We have recently proposed a new procedure, called Correlation Plenoptic Imaging (CPI), to overcome such fundamental limits by collecting plenoptic information through intensity correlation measurement. Using two correlated beams, from either a chaotic or an entangled photon source, we perform imaging in one arm and simultaneously obtain the angular information in the other arm. In this paper, we discuss the case in which the two correlated beams of light are generated by spontaneous parametric down-conversion. We review the principles of CPI with entangled photons and discuss its resolution and depth-of-field limits.

Depth Estimation Network for Dual Defocused Images with Different Depth-of-Field

Abstract: In this work, we propose an algorithm to estimate the depth map of a scene using defocused images. In particular, the depth map is estimated using two defocused images with different depth-of-field for the same scene. Similar to the approach of the general depth from defocus (DFD), the proposed algorithm obtains the depth information from the blurredness of the object. Moreover, our proposed algorithm dramatically improves the accuracy by using both the shallow and deep depth-of-field images, simultaneously. Especially, we propose a novel depth estimation network for dual defocused images using convolutional neural network (CNN). We evaluate our proposed network on the NYU-v2 dataset and show superior performance compared to the existing techniques.

Modified back-projection method in acoustic resolution-based photoacoustic endoscopy for improved lateral resolution.

Abstract: Purpose Acoustic resolution photoacoustic endoscopy (ARPAE) is an emerging tool for gastrointestinal tract and esophagus imaging. However, high resolution over large depth of field in ARPAE is still a challenge to be addressed due to the usage of fixed-focus transducers. Methods To solve this problem, a modified back-projection reconstruction algorithm is proposed in this work, which was demonstrated in two dimensions (2D). By employing data from multiple transducer positions and at the meantime considering the geometry of the transducer detection surface, this method offers a significantly improved lateral resolution throughout the imaging depth. The proposed method was evaluated with extensive numerical simulations and phantom experiments. Results Numerical simulations showed that the depth of focus (DOF) is greatly improved with our proposed method. Results also indicated that the improvement of the lateral resolution is more notable when the transducer has larger numerical aperture (NA) and higher central frequency, and the transducer focus is placed further from the rotation center. Experimental data further indicate there is a 30% to 40% improvement in the lateral resolution of targets in the out-of-focus region with our proposed new method. Conclusion This work can readily be employed in most current ARPAE systems to improve the obtained image quality, and may promote the advancement of ARPAE toward clinical applications.

Bifocal computational near eye light field displays and Structure parameters determination scheme for bifocal computational display.

Abstract: We propose a bifocal computational near eye light field display (bifocal computational display) and structure parameters determination scheme (SPDS) for bifocal computational display that achieves greater depth of field (DOF), high resolution, accommodation and compact form factor. Using a liquid varifocal lens, two single-focal computational light fields are superimposed to reconstruct a virtual object's light field by time multiplex and avoid the limitation on high refresh rate. By minimizing the deviation between reconstructed light field and original light field, we propose a determination framework to determine the structure parameters of bifocal computational light field display. When applied to different objective to SPDS, it can achieve high average resolution or uniform resolution display over scene depth range. To analyze the advantages and limitation of our proposed method, we have conducted simulations and constructed a simple prototype which comprises a liquid varifocal lens, dual-layer LCDs and a uniform backlight. The results of simulation and experiments with our method show that the proposed system can achieve expected performance well. Owing to the excellent performance of our system, we motivate bifocal computational display and SPDS to contribute to a daily-use and commercial virtual reality display.

Extending the Depth of Field beyond Geometrical Imaging Limitations Using Phase Noise as a Focus Measure in Multiwavelength Digital Holography

Abstract: Digital holography is a well-established technology for optical quality control in industrial applications. Two common challenges in digital holographic measurement tasks are the ambiguity at phase steps and the limited depth of focus. With multiwavelength holography, multiple artificial wavelengths are used to extend the sensor’s measurement range up to several millimeters, allowing measurements on rough surfaces. To further extend the unambiguous range, additional highly stabilized and increasingly expensive laser sources can be used. Besides that, unwrapping algorithms can be used to overcome phase ambiguities—but these require continuous objects. With the unique feature of numerical refocusing, digital holography allows the numerical generation of an all-in-focus unambiguous image. We present a shape-from-focus algorithm that allows the extension of the depth of field beyond geometrical imaging limitations and yields unambiguous height information, even across discontinuities. Phase noise is used as a focus criterion and to generate a focus index map. The algorithm’s performance is demonstrated at a gear flank with steep slopes and a step sample with discontinuities far beyond the system’s geometrical limit. The benefit of this method on axially extended objects is discussed.

Image processing method and device and equipment

Abstract: The invention provides an image processing method and device and equipment. The method comprises the steps of photographing a first master image through utilization of a master camera according to preset first exposure time and moreover, photographing an auxiliary image through utilization of an auxiliary camera according to the first exposure time; synthesizing the first master image and the auxiliary image to acquire a synthesized image; determining a first foreground area and a background area in the synthesized image according to depth of field information; determining second exposure timeaccording to luminance and a preset threshold and photographing a second master image according to the second exposure time; acquiring a second foreground area of the second master image according tocoordinate information of the first foreground area and carrying out image fusion processing on the second foreground area and the first foreground area to generate a foreground area of a high dynamic range image; and blurring out the background area and synthesizing the foreground area of the high dynamic range image and the burred background area to generate a target image. The generation efficiency and visual effect of the high dynamic range image are improved.

Three-dimensional imaging system with both improved lateral resolution and depth of field considering non-uniform system parameters.

Abstract: In this paper, we propose a new 3D passive image sensing and visualization technique to improve lateral resolution and depth of field (DoF) of integral imaging simultaneously. There is a resolution trade-off between lateral resolution and DoF in integral imaging. To overcome this issue, a large aperture and a small aperture can be used to record the elemental images to reduce the diffraction effect and extend the DoF, respectively. Therefore, in this paper, we utilize these two pickup concepts with a non-uniform camera array. To show the feasibility of our proposed method, we implement an optical experiment. For comparison in details, we calculate the peak signal-to-noise ratio (PSNR) as the performance metric.

Axial localization with modulated-illumination extended-depth-of-field microscopy.

Abstract: High-speed volumetric imaging represents a challenge in microscopy applications. We demonstrate a technique for acquiring volumetric images based on the extended depth of field microscopy with a fast focal scan and modulated illumination. By combining two frames with different illumination ramps, we can perform local depth ranging of the sample at speeds of up to half the camera frame rate. Our technique is light efficient, provides diffraction-limited resolution, enables axial localization that is largely independent of sample size, and can be operated with any standard widefield microscope based on fluorescence or darkfield contrast as a simple add-on. We demonstrate the accuracy of axial localization and applications of the technique to various dynamic extended samples, including in-vivo mouse brain.