This PDF file contains the front matter associated with SPIE Proceedings Volume 10410 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

This study evaluates the effects of beacon-wavelength mismatch on phase-compensation performance. In general, beacon-wavelength mismatch occurs at the system level because the beacon-illuminator laser (BIL) and high-energy laser (HEL) are often at different wavelengths. Such is the case, for example, when using an aperture sharing element to isolate the beam-control sensor suite from the blinding nature of the HEL. With that said, this study uses the WavePlex Toolbox in MATLAB® to model ideal spherical wave propagation through various atmospheric-turbulence conditions. To quantify phase-compensation performance, we also model a nominal adaptive-optics (AO) system. We achieve correction from a Shack-Hartmann wavefront sensor and continuous-face-sheet deformable mirror using a least-squares phase reconstruction algorithm in the Fried geometry and a leaky integrator control law. To this end, we plot the power in the bucket metric as a function of BIL-HEL wavelength difference. Our initial results show that positive BIL-HEL wavelength differences achieve better phase compensation performance compared to negative BIL-HEL wavelength differences (i.e., red BILs outperform blue BILs). This outcome is consistent with past results.

For small satellite remote sensing missions, a large aperture telescope more than 400mm is required to realize less than 1m GSD observations. However, it is difficult or expensive to realize the large aperture telescope using a monolithic primary mirror with high surface accuracy. A segmented mirror telescope should be studied especially for small satellite missions. Generally, not only high accuracy of optical surface but also high accuracy of optical alignment is required for large aperture telescopes. For segmented mirror telescopes, the alignment is more difficult and more important. For conventional systems, the optical alignment is adjusted before launch to achieve desired imaging performance. However, it is difficult to adjust the alignment for large sized optics in high accuracy. Furthermore, thermal environment in orbit and vibration in a launch vehicle cause the misalignments of the optics. We are developing an adaptive optics system using a MEMS deformable mirror for an earth observing remote sensing sensor. An image based adaptive optics system compensates the misalignments and wavefront aberrations of optical elements using the deformable mirror by feedback of observed images. We propose the control algorithm of the deformable mirror for a segmented mirror telescope by using of observed image. The numerical simulation results and experimental results show that misalignment and wavefront aberration of the segmented mirror telescope are corrected and image quality is improved.

The effects of deep turbulence in long-range imaging applications presents unique challenges to properly measure and correct for aberrations incurred along the atmospheric path. In practice, digital holography can detect the path-integrated wavefront distortions caused by deep turbulence, and di erent recording geometries offer different benefits depending on the application of interest. Previous studies have evaluated the performance of the off-axis image and pupil plane recording geometries for deep-turbulence sensing. This study models digital holography in the on-axis phase shifting recording geometry using wave optics simulations. In particular, the analysis models spherical-wave propagation through varying deep-turbulence conditions to estimate the complex optical field, and performance is evaluated by calculating the field-estimated Strehl ratio and RMS wavefront error. Altogether, the results show that digital holography in the on-axis phase shifting recording geometry is an effective wavefront-sensing method in the presence of deep turbulence.

The Shack-Hartmann Electron Densitometer (SHED) is a novel method to diagnose ultrashort pulse laser–produced plasmas by measuring the phase change to a probe laser beam. Using the Shack-Hartmann method, the phasefront of a probe laser is measured through a lenslet array onto a camera. Small changes in the location of the individual focal points on the camera plane are translated into changes in the probe beam’s wavefront. These wavefront distortions arise from refractive index variations caused by the free electrons. This method allows for superior performance in measuring minute variations in the electron density in 2 dimensions, single-shot, with sub-picosecond time resolution. The data taken with SHED demonstrated the ability to diagnose plasmas with densities of approximately 5x10¹⁷ cm^-3 and show the temporal evolution of the plasma long after the driving laser pulse has left. The method can be further improved by enclosing the probe beam and adding a second axis to allow for tomographic reconstruction of the electron density in 3 dimensions. By using SHED in a pump/probe setup, we are able to obtain temporally synchronized, ultrashort snap shots of the evolution of free electrons in laser-induced ionization of air and other transparent media.

This paper presents results from simultaneous measurements of fluid density and the resulting wavefront distortions in a sonic underexpanded jet. The density measurements were carried out using Rayleigh scattering, and the optical distortions were measured using a wavefront sensor based on phase shifting interferometry. The measurements represent a preliminary step toward relating wavefront distortions to a specific flow structure. The measured density field is used to compute the phase distortions using a wave propagation model based on a geometric-optics approximation, and the computed phase map shows moderate agreement with that obtained using the wavefront sensor.

Light field microscope (LFM) is an optical microscope capable of obtaining images having large depth of field with different viewpoints. By using the parallax of these multi-view images, it is possible to reconstruct the 3D sample. However, the sampling interval of this multi-viewpoint image depends on the pitch interval of the microlens array, so the spatial resolution is low, and the accuracy of the 3D sample to be reconstructed is also low. Conventional research has a method of increasing the spatial resolution by subpixel-shifted multiple images. However, this method has problems such as the necessity of mechanical operation and high cost. Therefore, we propose applying Fourier ptychography to the LFM. Fourier ptychography is a technique to obtain high spatial resolution images by joining images obtained by irradiating samples from different angles using LED arrays in Fourier space. Fourier ptychography does not require mechanical scanning and is high throughput and low cost. In addition, Fourier ptycoography is possible to obtain phase information on a sample, and it is also possible to obtain a fine 3D sample. We propose a method to generate high spatial resolution multiview images using Fourier ptychography and reconstruct highly accurate 3D sample from those images. In this research, we experiment with the original LFM and verify the effect.

This paper describes an approach to coherent aperture synthesis and fine-resolution image reconstruction employing phase retrieval from intensity measurements. A Gerchberg-Saxton algorithm is used on a pair of intensity measurements to obtain the fields for each telescope at each position of the telescopes without a reference beam. These are combined into a larger synthetic aperture, and a cross-correlation is used to sense and correct for piston, tip and tilt phases of each of the sub-apertures with respect one another, giving a much higher resolution than from a single aperture. Results of simulations, including the effects of speckle, are shown, and practical considerations are evaluated.

Recently we presented a Compressive Sensing Miniature Ultra-spectral Imaging System (CS-MUSI)1 . This system consists of a single Liquid Crystal (LC) phase retarder as a spectral modulator and a gray scale sensor array to capture a multiplexed signal of the imaged scene. By designing the LC spectral modulator in compliance with the Compressive Sensing (CS) guidelines and applying appropriate algorithms we demonstrated reconstruction of spectral (hyper/ ultra) datacubes from an order of magnitude fewer samples than taken by conventional sensors. The LC modulator is designed to have an effective width of a few tens of micrometers, therefore it is prone to imperfections and spatial nonuniformity. In this work, we present the study of this nonuniformity and present a mathematical algorithm that allows the inference of the spectral transmission over the entire cell area from only a few calibration measurements.

The FITTS correlation algorithm has been widely used for over forty years in high speed video trackers. It has the advantage that it is very simple implement in a digital processor with a small number of calculations. At each step the algorithm attempts to estimate the shift between an image of a moving target and a proto-type image. There are several well-known short comings of the FITTS algorithm. First the error in the shift estimate increases if the shift is greater than one pixel of the digital image. Secondly all correlation based tracking algorithms are susceptible to low frequency drift. These errors can force a lower tracker closed loop bandwidth to maintain track loop stability. In this paper we examine in an intuitive manner the underlying cause of the drift and approaches to its mitigation.

Spatial-heterodyne interferometry is a robust solution for deep-turbulence wavefront sensing. With that said, this paper analyzes the focal-plane array sampling requirements for spatial-heterodyne systems operating in the off-axis pupil plane recording geometry. To assess spatial-heterodyne performance, we use a metric referred to as the field-estimated Strehl ratio. We first develop an analytical description of performance with respect to the number of focal-plane array pixels across the Fried coherence diameter and then verify our results with wave-optics simulations. The analysis indicates that at approximately 5 focal-plane array pixels across the Fried coherence diameter, the field-estimated Strehl ratios begin to exceed 0:9 which is indicative of largely diffraction-limited results.

Digital holography holds several advantages over conventional imaging and wavefront sensing, chief among these being significantly fewer and simpler optical components and the retrieval of complex field. Consequently, many imaging and sensing applications including microscopy and optical tweezing have turned to using digital holography. A significant obstacle for digital holography in real-time applications, such as wavefront sensing for high energy laser systems and high speed imaging for target racking, is the fact that digital holography is computationally intensive; it requires iterative virtual wavefront propagation and hill-climbing to optimize some sharpness criteria. It has been shown recently that minimum-variance wavefront prediction can be integrated with digital holography and image sharpening to reduce significantly large number of costly sharpening iterations required to achieve near-optimal wavefront correction. This paper demonstrates further gains in computational efficiency with localized sharpening in conjunction with predictive dynamic digital holography for real-time applications. The method optimizes sharpness of local regions in a detector plane by parallel independent wavefront correction on reduced-dimension subspaces of the complex field in a spectral plane.

In MRI the presence of metal implants causes severe artifacts in images and interferes with the usual techniques used to separate fat signals from other tissues. In the Dixon method, three images are acquired at different echo times to enable the variation in the magnetic field to be estimated. However, the estimate is represented as the phase of a complex quantity and therefore suffers from wrapping. High field gradients near the metal mean that the phase estimate is undersampled and therefore challenging to unwrap.

We have developed POP, phase estimation by onion peeling, an algorithm which unwraps the phase along 1-D paths for a 2-D image obtained with the Dixon method. The unwrapping is initially performed along a closed path enclosing the implant and well separated from it. The recovered phase is expanded using a smooth periodic basis along the path. Then, path-by-path, the estimate is applied to the next path and then the expansion coefficients are estimated to best fit the wrapped measurements. We have successfully tested POP on MRI images of specially constructed phantoms and on a group of patients with hip implants.

In principle, POP can be extended to 3-D imaging. In that case, POP would entail representing phase with a suitably smooth basis over a series of surfaces enclosing the implant (the "onion skins"), again beginning the phase estimation well away from the implant. An approach for this is proposed.

Results are presented for fat and water separation for 2-D images of phantoms and actual patients. The practicality of the method and its employment in clinical MRI are discussed.

Understanding the 3D structure of the environment is advantageous for many tasks in the field of robotics and autonomous vehicles. From the robot’s point of view, 3D perception is often formulated as a depth image reconstruction problem. In the literature, dense depth images are often recovered deterministically from stereo image disparities. Other systems use an expensive LiDAR sensor to produce accurate, but semi-sparse depth images. With the advent of deep learning there have also been attempts to estimate depth by only using monocular images. In this paper we combine the best of the two worlds, focusing on a combination of monocular images and low cost LiDAR point clouds. We explore the idea that very sparse depth information accurately captures the global scene structure while variations in image patches can be used to reconstruct local depth to a high resolution. The main contribution of this paper is a supervised learning depth reconstruction system based on a deep convolutional neural network. The network is trained on RGB image patches reinforced with sparse depth information and the output is a depth estimate for each pixel. Using image and point cloud data from the KITTI vision dataset we are able to learn a correspondence between local RGB information and local depth, while at the same time preserving the global scene structure. Our results are evaluated on sequences from the KITTI dataset and our own recordings using a low cost camera and LiDAR setup.

This paper describes a 3D point cloud upsampling method using the fusion of 2D (e.g., EO, electro-optical) and 3D (e.g., LIDAR, light detection and ranging) sensors, that can improve the performance of 3D registration of the input point cloud with a known 3D model. In this method, a denser 3D point cloud is generated by using the corresponding EO pixel intensity values in the upsampling process. In order to increase the upsampling accuracy based on the scene complexity of a local surface area, the EO pixel entropy of the local area is used. Depending on the local entropy values (low, medium, and high), we apply different upsampling procedures (mean upsampling, full upsampling, and no upsampling respectively). By using the proposed method of upsampling, missing holes in the point cloud are filled in and the overall point density is increased, which results in improved accuracy in 3D registration of the input point cloud with its known 3D model.

This paper describes a method for continuous 3D registration of an object using a 3D sensor and model of the object, significantly speeding up an iterative alignment method by using a 2D array cache. The cache stores local subtrees in a kd-tree search to initialize the processing of subsequent data. The cache is spatially structured to match the projection of the 3D space into the sensor’s field of view, and automatically adapts when points cross discontinuities or occlusion boundaries. Experiments in a simulated 3D tracking and relative maneuvering scenario demonstrate the computational speedup benefits of local subtree caching.

An additional step to traditional iterative phase retrieval algorithms is described that allows the reconstruction of an image from the product of its sufficiently sampled far-field diffraction intensity and a second signal. The iterative phasing algorithm with this added step attempts to recover both the image and the multiplicative signal at the same time. Simulations show that reasonable reconstructions of both the original image and the multiplicative signal can be attained in many cases.

Key elements of the fast adaptive optical system (AOS), having correction frequency of 1400 Hz, for atmospheric turbulence compensation, are described in this paper. A water-cooled bimorph deformable mirror with 46 electrodes, as well as stacked actuator deformable mirror with 81 piezoactuators and 2000 Hz Shack-Hartmann wavefront sensor were considered to be used to control the light beam. The parameters of the turbulence at the 1.2 km path of the light propagation were measured and analyzed. The key parameters for such an adaptive system were worked out.

Distortions of the scattered laser beam (λ=0.65μm) were numerically estimated by means of Shack-Hartmann technique and experimentally measured. The ability to focus laser beam, passed through the scattering suspension of polystyrene microspheres in distilled water, using bimorph deformable mirror was investigated both numerically and experimentally. Shack-Hartmann technique was used to measure the local slopes of the Poynting vector, and CCD camera was used to analyze the intensity distribution of the focal spot in the far-field. Bimorph deformable mirror with 14 electrodes was utilized in order to increase the focusing efficiency of the laser beam. The voltages to be applied to the mirror electrodes were calculated using three techniques: LSQ (least squares) fit-error minimization by Shack-Hartmann sensor, Hillclimbing optimization by Shack-Hartmann sensor and Hill-climbing optimization by the far-field CCD camera.

The recent availability of ultra-bright and ultra-short X-rays pulses from new sources called x-ray free-electron lasers (XFELs) has introduced a new paradigm in X-ray crystallography. Called "diffraction-before-destruction," this paradigm addresses the main problems that plague crystallography using synchrotron sources. However, the phase problem of coherent diffraction imaging remains: one has to retrieve the phase of the measured diffraction amplitude in order to reconstruct the object. Fibrous and membrane proteins that crystallize in 1D and 2D crystals can now potentially be used for data collection with free-electron lasers. The crystallographic phase problem with such crystalline specimens is eased as the Fourier amplitude can be sampled more finely than at the Bragg sampling along one or two directions. Here we characterise uniqueness of the phase problem for different types of crystalline specimen. Simulated ab initio phase retrieval using iterative projection algorithms for 2D crystals is presented.

Shadow imaging is a technique to obtain highly resolved silhouettes of resident space objects (RSOs) which would otherwise be unattainable using conventional terrestrial based imaging approaches. This is done by post processing the measured irradiance pattern (shadow) cast onto the Earth as the RSO occults a star. The research presented here focuses on recent developments in shadow imaging of geosynchronous (GEO) satellites with near stationary orbits approximately 36,000 km from the Earth. The fundamental resolution limits of shadow imaging are quantified analytically as a function of spectral bin width of the collected light. A set of simulated imagery is shown to agree with analytically derived resolution limits.

We describe the concept of controlled or natural motion of an object in a spatially varying field as the basis for high resolution coherent optical sensing and imaging. Examples presented are for microscopy with interfering laser beams and speckle formed by scattering from a random medium. Biomedical and environmental applications are presented and resolution issues discussed.

In the SPIE 2016 Unconventional Imaging session, the authors laid out a breakthrough new theory for active array imaging that exploits the speckle return to generate a high-resolution picture of the target. Since then, we have pursued that theory even in long-range (<1000-km) engagement scenarios and shown how we can obtain that high-resolution image of the target using only a few illuminators, or by using many illuminators. There is a trade of illuminators versus receivers, but many combinations provide the same synthetic aperture resolution. We will discuss that trade, along with the corresponding radiometric and speckle-imaging Signal-to-Noise Ratios (SNR) for geometries that can fit on relatively small aircraft, such as an Unmanned Aerial Vehicle (UAV). Furthermore, we have simulated the performance of the technique, and we have created a laboratory version of the approach that is able to obtain high-resolution speckle imagery. The principal results presented in this paper are the Signal to Noise Ratios (SNR) for both the radiometric and the speckle imaging portions of the problem, and the simulated results obtained for representative arrays.

Underwater turbulence occurs due to random fluctuations of temperature and salinity in the water. These fluctuations are responsible for variations in water density, refractive index and attenuation. These impose random geometric distortions, spatio-temporal varying blur, limited range visibility and limited contrast on the acquired images. There are some restoration techniques developed to address this problem, such as image registration based, lucky region based and centroid-based image restoration algorithms. Although these methods demonstrate better results in terms of removing turbulence, they require computationally intensive image registration, higher CPU load and memory allocations. Thus, in this paper, a simple patch based dictionary learning algorithm is proposed to restore the image by alleviating the costly image registration step. Dictionary learning is a machine learning technique which builds a dictionary of non-zero atoms derived from the sparse representation of an image or signal. The image is divided into several patches and the sharp patches are detected from them. Next, dictionary learning is performed on these patches to estimate the restored image. Finally, an image deconvolution algorithm is employed on the estimated restored image to remove noise that still exists.

Imaging through underwater experiences severe distortions due to random fluctuations of temperature and salinity in water, which produces underwater turbulence through diffraction limited blur. Lights reflecting from objects perturb and attenuate contrast, making the recognition of objects of interest difficult. Thus, the information available for detecting underwater objects of interest becomes a challenging task as they have inherent confusion among the background, foreground and other image properties. In this paper, a saliency-based approach is proposed to detect the objects acquired through an underwater turbulent medium. This approach has drawn attention among a wide range of computer vision applications, such as image retrieval, artificial intelligence, neuro-imaging and object detection. The image is first processed through a deblurring filter. Next, a saliency technique is used on the image for object detection. In this step, a saliency map that highlights the target regions is generated and then a graph-based model is proposed to extract these target regions for object detection.

We present a user-friendly computational platform for numerical analysis and performance assessment of a wide range of optical systems for imaging and laser projection applications. The wave-optics-based software package, referred to as the Wave-Optics Joint Estimation Toolbox (WaveJET), exploits advantages of GPU/CUDA technologies and allows one to quickly and accurately simulate atmospheric turbulence effects on various systems using the combination of different computational techniques and algorithms for wavefront sensing and imaging. The WaveJET software also takes into account critical system engineering design constraints including system architecture, diffraction, turbulence propagation, adaptive optics control, and coherent/incoherent beam combining.

Typically, the cost of a space-borne imaging system is driven by the size and mass of the primary aperture. The solution that we propose uses a method to construct an imaging system in space in which the nonlinear optical properties of a cloud of micron-sized particles, shaped into a specific surface by electromagnetic means, and allows one to form a very large and lightweight aperture of an optical system, hence reducing overall mass and cost. Recent work at JPL has investigated the feasibility of a granular imaging system, concluding that such a system could be built and controlled in orbit. We conducted experiments and simulation of the optical response of a granular lens. In all cases, the optical response, measured by the Modulation Transfer Function, of hexagonal reflectors was closely comparable to that of a conventional spherical mirror. We conducted some further analyses by evaluating the sensitivity to fill factor and grain shape, and found a marked sensitivity to fill factor but no sensitivity to grain shape. We have also found that at fill factors as low as 30%, the reflection from a granular lens is still excellent. Furthermore, we replaced the monolithic primary mirror in an existing integrated model of an optical system (WFIRST Coronagraph) with a granular lens, and found that the granular lens that can be useful for exoplanet detection provides excellent contrast levels. We will present our testbed and simulation results in this paper.

A homogenizing light pipe provides an avenue of altering the spatial coherence properties of a laser source for the purpose of reducing speckle in an active imaging scenario. In this study, an efficient coherent mode decomposition was used to characterize the effect of a light pipe on the coherence properties of an input beam. The waveguide was found to reduce the global degree of coherence of the input beam in a manner which made the beam more suitable as an illuminator for speckle free imaging. The beam exiting the waveguide was found to contain a banded mutual coherence function due to the strong presence of symmetry within the setup. A formula for predicting the speckle contrast in the presence of these bands is proposed in the paper and appears to show great agreement with simulated results. The efficient propagation method and speckle contrast formula presented in the paper should be useful in designing an optimal active illumination source for reducing speckle.

The analysis of optical propagation through both deterministic and stochastic refractive-index fields may be substantially simplified if diffraction effects can be neglected. With regard to simplification, it is known that certain geometricaloptics predictions often agree well with field observations but it is not always clear why this is so. Here, a new investigation of this issue is presented involving wave optics and geometrical (ray) optics computer simulations of a beam of visible light propagating through fully turbulent, homogeneous and isotropic refractive-index fields. We compare the computationally simulated, aperture-averaged angle-of-arrival variances (for aperture diameters ranging from 0.5 to 13 Fresnel lengths) with theoretical predictions based on the Rytov theory.

Laser scanning is widely used in on-line industrial 3D inspection, cultural heritage conservation and reverse engineering. However, in the traditional laser scanning, the most widely-used approach is based on the projection of a single directional laser stripe over an object. Because the width of the laser stripe is physically difficult to compress enough to be fine at the edge of the object, the traditional measurement method is not accurate for edge measurements. This paper proposes an edge sensitive 3D measurement system which is fast and accurate, using two directional laser stripes scanning with a laser projector. Scanning metal edge steps and complex surface edge with this system only require a single scanning to perform 3D reconstruction. So this scanning method has the advantages of high efficiency, high speed and edges with high precision.

Down-looking synthetic aperture imaging ladar have bidirectional (positive and negative direction) modulation in the orthogonal direction of travel during phase modulation. The return signal can be also collected in the two directions. The imaging processing with bidirectional modulation is used and demonstrated. The signal-to-noise ratio can be enhanced in this mode synthetic aperture imaging ladar. Meanwhile, the velocity of carrying-platform can be faster. In the experiment, the return signals with bidirectional modulation are stacked and rebuilt. Compared to the unidirectional modulation imaging, the faster and clearer imaging is realized with bidirectional modulation. This technique has a great potential for applications in extensive synthetic aperture imaging ladar fields.

Synthetic aperture imaging ladar (SAIL) is one of the most possible optical active imaging methods to break the diffraction limit and achieve super-resolution in a long distance. Nevertheless, two-dimensional reconstructed images of the natural targets have not been achieved. Polarization state change of the backscattered light, which is always determined by the interaction of the light and the materials on the target plane, will affect the imaging of SAIL. The Mueller matrices can describe the complex polarization features of the target reflection and treat this interaction. In this paper, a measurement of the Mueller matrices for different target materials will be designed, and the influences of polarization characteristic of targets on resolution element imaging in side-looking and down-looking SAILs will be theoretically analyzed.

We usually need to measure an object at multiple angles in the traditional optical three-dimensional measurement method, due to the reasons for the block, and then use point cloud registration methods to obtain a complete threedimensional shape of the object. The point cloud registration based on a turntable is essential to calculate the coordinate transformation matrix between the camera coordinate system and the turntable coordinate system. We usually calculate the transformation matrix by fitting the rotation center and the rotation axis normal of the turntable in the traditional method, which is limited by measuring the field of view. The range of exact feature points used for fitting the rotation center and the rotation axis normal is approximately distributed within an arc less than 120 degrees, resulting in a low fit accuracy. In this paper, we proposes a better method, based on the invariant eigenvalue principle of rotation matrix in the turntable coordinate system and the coordinate transformation matrix of the corresponding coordinate points. First of all, we control the rotation angle of the calibration plate with the turntable to calibrate the coordinate transformation matrix of the corresponding coordinate points by using the least squares method. And then we use the feature decomposition to calculate the coordinate transformation matrix of the camera coordinate system and the turntable coordinate system. Compared with the traditional previous method, it has a higher accuracy, better robustness and it is not affected by the camera field of view. In this method, the coincidence error of the corresponding points on the calibration plate after registration is less than 0.1mm.

Texture fusion is essential for three-dimensional photo-realistic texture model reconstruction. Multi-resolution texture fusion mainly is applied to reconstruct three-dimensional models that the local texture is very realistic. This paper presents a multi-resolution texture fusion algorithm based on digital image processing. The technique utilizes the depth camera to obtain range data and the texture camera to obtain the local interested high-resolution texture of the object. And each point of the range images a pixel of the picture corresponding to this view should be found, so the map between high-resolution texture and local three-dimensional points could be obtained by calibrating texture camera and depth camera to register the local high-resolution texture with the low resolution texture collected by the depth camera or calibrating texture camera and depth camera. ICP is applied to register models, and mapping different-resolution textures onto three-dimensional models. Then the light source correction of textures is applied to remove the systematic differences such as lighting change which are achieved by computing linear regression correction mode. Finally, the global correction is applied to refine the observable variations in color that may still exist near the fusion boundaries. These corrections will utilize the grid triangle vertex color as a constraint to drive texture fusion to remove discontinuities from different resolutions. The advantage of this technique is that it utilizes the light source correction and global correction to fuse different resolutions textures. Experiments with this technique indicate that it significantly corrects the observable discontinuities within the overlapping areas, which are given from different resolutions, lighting change, non-lambertian object surface, etc.

There is no denying that 360-degree shape measurement technology plays an important role in the field of threedimensional optical metrology. Traditional optical 360-degree shape measurement methods are mainly two kinds: the first kind, by placing multiple scanners to achieve 360-degree measurements; the second kind, through the high-precision rotating device to get 360-degree shape model. The former increases the number of scanners and costly, while the latter using rotating devices lead to time consuming. This paper presents a low cost and fast optical 360-degree shape measurement method, which possesses the advantages of full static, fast and low cost. The measuring system consists of two mirrors with a certain angle, a laser projection system, a stereoscopic calibration block, and two cameras. And most of all, laser MEMS scanner can achieve precise movement of laser stripes without any movement mechanism, improving the measurement accuracy and efficiency. What’s more, a novel stereo calibration technology presented in this paper can achieve point clouds data registration, and then get the 360-degree model of objects. A stereoscopic calibration block with special coded patterns on six sides is used in this novel stereo calibration method. Through this novel stereo calibration technology we can quickly get the 360-degree models of objects.

Super-resolution is being considered as one of the important goals for optical imaging and image processing. In this paper, we present a novel imaging technique that exceeds the limit of resolving power of diffraction-limited optical system to achieve super-resolution imaging, by combining the advantages of compressive sensing and complex annular filters. This technique is realized by utilizing a classical 4F optical system with a phase-only spatial light modulator. Furthermore, the feasibility of this technique is theoretically analyzed, and physically validated by laboratory experiments. Experimental results demonstrated that this technique improves the resolving power of diffraction-limited optical system by approximately 1.58 times, and the intensity image of high-resolution object can be recovered from 25% of the total number of measurements.

This research is aimed to develop a new method of polymer dispersed liquid crystal morphology estimation. The possibility of application of the Mueller matrix approach for separate observation of isotropic and anisotropic parts of different PDLC samples was illustrated in it.

A photon counting 3D imaging system with short-pulsed structured illumination and a single-pixel photon counting detector is built. The proposed multiresolution photon counting 3D imaging technique acquires a high-resolution 3D image from a coarse image and details at successfully finer resolution sampled by Hadamard multiplexing along with the wavelet trees. The detected power is significant increased thanks to the Hadamard multiplexing. Both the required measurements and the reconstruction time can be significant reduced, which makes the proposed technique suitable for scenes with high spatial resolution. Since the depth map is retrieved through a linear inverse Hadamard transform instead of the computational intensive optimization problems performed in CS, the time consumed to retrieve the depth map can be also reduced, and thus it will be suitable for applications of real-time compressed 3D imaging such as object tracking. Even though the resolution of the final 3D image can be high, the number of measurements remains small due to the adaptivity of the wavelet-trees-based sampling strategy. The adaptive sampling technique is quality oriented, allowing more control over the image quality. The experimental results indicate that both the intensity image and depth map of a scene at resolutions up to 512×512 pixels can be acquired and retrieved with practical times as low as 17 seconds.

Virtual reality applications provide an opportunity to test human vision in well-controlled scenarios that would be difficult to generate in real physical spaces. This paper presents a study intended to evaluate the importance of the regularity priors used by the human visual system. Using a CAVE simulation, subjects viewed virtual objects in a variety of experimental manipulations. In the first experiment, the subject was asked to count the objects in a scene that was viewed either right-side-up or upside-down for 4 seconds. The subject counted more accurately in the right-side-up condition regardless of the presence of binocular disparity or color. In the second experiment, the subject was asked to reconstruct the scene from a different viewpoint. Reconstructions were accurate, but the position and orientation error was twice as high when the scene was rotated by 45°, compared to 22.5°. Similarly to the first experiment, there was little difference between monocular and binocular viewing. In the third experiment, the subject was asked to adjust the position of one object to match the depth extent to the frontal extent among three objects. Performance was best with symmetrical objects and became poorer with asymmetrical objects and poorest with only small circular markers on the floor. Finally, in the fourth experiment, we demonstrated reliable performance in monocular and binocular recovery of 3D shapes of objects standing naturally on the simulated horizontal floor. Based on these results, we conclude that gravity, horizontal ground, and symmetry priors play an important role in veridical perception of scenes.

There are several algorithms to solve the shape from shading problem. Most of these algorithms rely on basic assumptions about the surface reflective properties, camera projection and location, and the light source distribution. In this paper, we implemented an algorithm based on a Wiener filter to obtain surface estimation from a single image. We tested the algorithm with images generated synthetically and also employing images from a real object. We were able to obtain the shape of objects with different geometries.