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Summary We utilize digital image‐plane holographic microscopy (DIPHM) to achieve the real‐time surface profile measurement of microstructure. The impulse response functions of DIPHM and traditional digital holographic microscopy (DHM) are both derived. The theoretical derivations indicate that the differences between the two techniques are caused by the diffraction effect of the recording plane with a finite size. The diffraction effect would introduce an unstable factor to the wavefront reconstruction. Therefore, the DIPHM has the characteristics of totally full field of view and low measuring noise compared to DHM. In addition, we take DIPHM and DHM in dual‐wavelength mode as a special example to confirm the points above. From both experimental results and theoretical analysis, DIPHM is demonstrated to be an optimized technique with high‐quality imaging, especially benefiting the situation where multi‐wavelength measurement is required. This method is robust against environmental noise. SCANNING 38:288–296, 2016. © 2015 Wiley Periodicals, Inc.

The numerical aperture of the spectrometer is crucial for weak signal detection. The transmission lens-based configuration has more optimization variations, and the grating can work approximately in the Littrow condition; thus, it is easier to acquire high numerical aperture (NA). However, designing a large aperture focusing lens remains challenging, and thus, ultra-high NA spectrometers are still difficult to acquire. In this paper, we propose a method of setting image plane tilt ahead directly when designing the large aperture focusing lens to simplify the high NA spectrometer design. By analyzing the accurate demands of the focusing lens, it can be concluded that a focusing lens with image plane tilt has much weaker demand for achromatism, and other monochromatic aberration can also be reduced, which is helpful to increase the NA. An NA0.5 fiber optic spectrometer design is given to demonstrate the proposed method. The design results show that the NA can achieve 0.5 using four lenses of two materials, and the MTF is higher than 0.5 when the spectral dispersion length is 12.5 mm and the pixel size is 25 μm, and thus, the spectral resolution can achieve 6.5 nm when the spectral sampling ratio is 2:1. The proposed method can provide reference for applications when appropriate materials are limited and high sensitivity is necessary.

Light field (LF) image depth estimation is a critical technique for LF-related applications such as 3D reconstruction, target detection, and tracking. The refocusing property of LF images provide rich information for depth estimations; however, it is still challenging in cases of occlusion regions, edge regions, noise interference, etc. The epipolar plane image (EPI) of LF can effectively deal with the depth estimation because of its characteristics of multidirectionality and pixel consistency—in which the LF depth estimations are converted to calculate the EPI slope. This paper proposed an EPI LF depth estimation algorithm based on a directional relationship model and attention mechanism. Unlike the subaperture LF depth estimation method, the proposed method takes EPIs as input images. Specifically, a directional relationship model was used to extract direction features of the horizontal and vertical EPIs, respectively. Then, a multiviewpoint attention mechanism combining channel attention and spatial attention is used to give more weight to the EPI slope information. Subsequently, multiple residual modules are used to eliminate the redundant features that interfere with the EPI slope information—in which a small stride convolution operation is used to avoid losing key EPI slope information. The experimental results revealed that the proposed algorithm outperformed the compared algorithms in terms of accuracy.

Targetless LiDAR–camera extrinsic calibration remains challenging due to unreliable cross-modal correspondences and sensitivity to initialization. We present a targetless extrinsic calibration framework based on class-agnostic boundary mask alignment in a shared image-plane representation. This scheme first constructs consistent LiDAR–camera mask pairs from image-plane depth and intensity projections of LiDAR data and camera images. It then obtains robust initial pose candidates through bounded rotation-only global initialization and refines them using a computationally efficient stochastic gradient approximation to estimate the optimal extrinsic parameters. Experiments on the KITTI benchmark demonstrate a superior accuracy–runtime trade-off compared with a segmentation-based global optimization baseline, while real-world driving tests confirm stable cross-modal alignment under vibration and inter-modal timing jitter.

The popularization of multi-camera systems and multi-view image capture has led to the emergence of sparse multi-view image super-resolution (MVSR) as a promising research direction. The primary approach to sparse multi-view super-resolution (SR) currently involves extending traditional single-view SR and stereo SR frameworks, i.e., extracting features from each view and performing pixel-domain alignment and fusion to leverage cross-view reference information. However, this straightforward framework has two main drawbacks. First, performing cross-view fusion in the pixel domain disregards the spatial perception information that multi-view images provide. Second, feature alignment and fusion across views introduce considerable redundant and repetitive computations, which hinders further scalability to more viewpoints. This paper proposes a novel sparse multi-view SR framework based on a unified spatial representation reference. Specifically, the proposed method first computes a multi-plane image spatial representation from the multi-view images. This multi-plane image (MPI) representation encapsulates all the information from each view and has spatial perception. Subsequently, an upsampled reference image is rendered from the MPI representation for the low-resolution views. A high-low frequency separation fusion network is then proposed to upscale the input low-resolution images based on the rendered reference. Experimental results demonstrate the effectiveness of the proposed method for recovering high-frequency details.

Conventional uniaxial techniques generally require shifting objects or projection grating with the assistance of a high-precision mechanical moving component. To overcome this limitation, we propose a novel uniaxial 3-D shape measurement system with auto-synchronous phase-shifting and defocusing by using a tilted and fixed projection grating. The tilted focused image plane (FIP), which is reflected by a mirror at about 90 degrees, could be shifted across the measured surface by slightly rotating the mirror within a small angle range. This procedure will simultaneously introduce the change in defocusing and phase-shifting of the fringe. The modulation curve of each point can be deciphered by Fourier fringe analysis after a sequence of fringe intensities is acquired. Since both the measured object and projection grating are fixed, the proposed method could make the measurement system more compact and flexible. Both computer simulation and experiments are carried out to demonstrate the validity of this proposed system.

An effective approach based on conformal mapping and distributed dislocation techniques is proposed to solve the plane problem of an arbitrarily curved crack in an infinite homogeneous and isotropic elastic plane under uniform remote in-plane stresses. A Cauchy-type singular integral equation is constructed in the image plane. The singular integral equation is solved numerically using the Gauss–Chebyshev integration formula to arrive at the stress intensity factors at the two crack tips. Numerical examples of an elliptical arc crack, a hypotrochoidal arc crack and a cycloid crack are presented to demonstrate the effectiveness of the proposed solution method.

In this paper, virtual reality development software Virtools is used as the main tool for scene rendering in distributed virtual reality systems. The nearest distance-based control strategy is designed to achieve synchronous interaction between nodes by utilizing consistency control and prediction algorithms for nodes in the same cluster. Optimize the virtual scene by using texture mapping, detail level, and dynamic loading techniques. Propose a distributed parallel drawing method based on image plane segmentation to accelerate image drawing. The analysis and testing of the multiuser virtual reality roaming system in this paper is completed from four aspects: offline training model evaluation amount roaming test, lock performance test, and operation sequence error correction test. Among the analyzed data, the average lock time overhead is 123us, which has no obvious connection with the number of nodes, the number of locking times, and the execution of operations. According to each lock overhead of 123us, the number of concurrencies allowed per second is 8563, which fully meets the requirements of the system at this stage.

Time-domain backprojection algorithms are widely used in state-of-the-art synthetic aperture radar (SAR) imaging systems that are designed for applications where motion error compensation is required. These algorithms include an interpolation procedure, under which an unknown SAR range-compressed data parameter is estimated based on complex-valued SAR data samples and backprojected into a defined image plane. However, the phase of complex-valued SAR parameters estimated based on existing interpolators does not contain correct information about the range distance between the SAR imaging system and the given point of space in a defined image plane, which affects the quality of reconstructed SAR scenes. Thus, a phase-control procedure is required. This paper introduces extensions of existing linear, cubic, and sinc interpolation algorithms to interpolate complex-valued SAR data, where the phase of the interpolated SAR data value is controlled through the assigned a priori known range time that is needed for a signal to reach the given point of the defined image plane and return back. The efficiency of the extended algorithms is tested at the Nyquist rate on simulated and real data at THz frequencies and compared with existing algorithms. In comparison to the widely used nearest-neighbor interpolation algorithm, the proposed extended algorithms are beneficial from the lower computational complexity perspective, which is directly related to the offering of smaller memory requirements for SAR image reconstruction at THz frequencies.

To evaluate the diagnostic performance of susceptibility map-weighted imaging (SMwI) taken in different acquisition planes for discriminating patients with neurodegenerative parkinsonism from those without. This retrospective, observational, single-institution study enrolled consecutive patients who visited movement disorder clinics and underwent brain MRI and F-FP-CIT PET between September 2021 and December 2021. SMwI images were acquired in both the oblique (perpendicular to the midbrain) and the anterior commissure-posterior commissure (AC-PC) planes. Hyperintensity in the substantia nigra was determined by two neuroradiologists. F-FP-CIT PET was used as the reference standard. Inter-rater agreement was assessed using Cohen's kappa coefficient. The diagnostic performance of SMwI in the two planes was analyzed separately for the right and left substantia nigra. Multivariable logistic regression analysis with generalized estimating equations was applied to compare the diagnostic performance of the two planes. In total, 194 patients were included, of whom 105 and 103 had positive results on F-FP-CIT PET in the left and right substantia nigra, respectively. Good inter-rater agreement in the oblique (κ = 0.772/0.658 for left/right) and AC-PC planes (0.730/0.741 for left/right) was confirmed. The pooled sensitivities for two readers were 86.4% (178/206, left) and 83.3% (175/210, right) in the oblique plane and 87.4% (180/206, left) and 87.6% (184/210, right) in the AC-PC plane. The pooled specificities for two readers were 83.5% (152/182, left) and 82.0% (146/178, right) in the oblique plane, and 83.5% (152/182, left) and 86.0% (153/178, right) in the AC-PC plane. There were no significant differences in the diagnostic performance between the two planes ( > 0.05). There are no significant difference in the diagnostic performance of SMwI performed in the oblique and AC-PC plane in discriminating patients with parkinsonism from those without. This finding affirms that each institution may choose the imaging plane for SMwI according to their clinical settings.