Phase retrieval from single-shot square wave fringe based on image denoising using deep learning.

Fringe-structured light measurement technology has garnered significant attention in recent years. To enhance measurement speed while maintaining a certain level of accuracy using binary fringe, this paper proposes a phase retrieval method with single-frame binary square wave fringe. The proposed method utilizes image denoising through deep learning to extract the phase, enabling the use of a trained image denoiser as a low-pass filter, which adaptively replaces the manual selection of the appropriate band-pass filter.

3D face recognition algorithm based on deep Laplacian pyramid under the normalization of epidemic control.

Under the normalization of epidemic control in COVID-19, it is essential to realize fast and high-precision face recognition without feeling for epidemic prevention and control. This paper proposes an innovative Laplacian pyramid algorithm for deep 3D face recognition, which can be used in public. Through multi-mode fusion, dense 3D alignment and multi-scale residual fusion are ensured.

Theoretical analysis and experimental investigation of the Floyd-Steinberg-based fringe binary method with offset compensation for accurate 3D measurement.

Digital fringe projection (DFP) with defocused binary fringe patterns has the ability to overcome the projector nonlinearity and achieve a high-speed 3D measurement. The Floyd-Steinberg (FS) dithering technique is one of the most commonly adopted binary fringe coding methods due to its relatively high measurement accuracy. Nevertheless, we found that the FS binary fringe would cause a fixed error in the recovered phase, which is proven to be invariable for various defocusing levels and various phase-shift steps according to the analysis of the phase error based on noise model of phase-shifting profilometry.

A selective kernel-based cycle-consistent generative adversarial network for unpaired low-dose CT denoising.

Low-dose computed tomography (LDCT) denoising is an indispensable procedure in the medical imaging field, which not only improves image quality, but can mitigate the potential hazard to patients caused by routine doses. Despite the improvement in performance of the cycle-consistent generative adversarial network (CycleGAN) due to the well-paired CT images shortage, there is still a need to further reduce image noise while retaining detailed features. Inspired by the residual encoder-decoder convolutional neural network (RED-CNN) and U-Net, we propose a novel unsupervised model using CycleGAN for LDCT imaging, which injects a two-sided network into selective kernel networks (SK-NET) to adaptively select features, and uses the patchGAN discriminator to generate CT images with more detail maintenance, aided by added perceptual loss.

Optical random phase dropout in a diffractive deep neural network.

Unitary learning is a backpropagation (BP) method that serves to update unitary weights in fully connected deep complex-valued neural networks, meeting a prior unitary in an active modulation diffractive deep neural network. However, the square matrix characteristic of unitary weights in each layer results in its learning belonging to a small-sample training, which produces an almost useless network that has a fairly poor generalization capability. To alleviate such a serious over-fitting problem, in this Letter, optical random phase dropout is formulated and designed.

3-D face registration solution with speckle encoding based spatial-temporal logical correlation algorithm.

3-D information acquisition (registration) of whole face plays a significant role in 3-D human face recognition application. In this paper, we develop a prototype of 3-D system consisting of two binocular measurement units that allows a full 3-D reconstruction by utilizing the advantages of a novel correlation algorithm. In this system, we use optical modulation to produce temporally and spatially varying high-density binary speckle patterns to encode the tested face, then propose a spatial-temporal logical correlation (STLC) stereo matching algorithm to fast determine the accurate disparity with a coarse and refined strategy.

Sparsity-based continuous wave terahertz lens-free on-chip holography with sub-wavelength resolution.

We demonstrate terahertz (THz) lens-free in-line holography on a chip in order to achieve 40 μm spatial resolution corresponding to ~0.7λ with a numerical aperture of ~0.87.

Experimental study of temporal-spatial binary pattern projection for 3D shape acquisition.

Three-dimensional (3D) acquisition of an object with modest accuracy and speed is of particular concern in practice. The performance of digital sinusoidal fringe pattern projection using an off-the-shelf digital video projector is generally discounted by the nonlinearity and low switch rate. In this paper, a binary encoding method to encode one computer-generated standard sinusoidal fringe pattern is presented for circumventing such deficiencies.

Accurate and fast 3D surface measurement with temporal-spatial binary encoding structured illumination.

Balancing the accuracy and speed for 3D surface measurement of object is crucial in many important applications. Binary encoding pattern utilizing the high-speed image switching rate of digital mirror device (DMD)-based projector could be used as the candidate for fast even high-speed 3D measurement, but current most schemes only enable the measurement speed, which limit their application scopes. In this paper, we present a binary encoding method and develop an experimental system aiming to solve such a situation.

Modulation measuring profilometry with auto-synchronous phase shifting and vertical scanning.

To determine the shape of a complex object with vertical measurement mode and higher accuracy, a novel modulation measuring profilometry realizing auto-synchronous phase shifting and vertical scanning is proposed. Coaxial optical system for projection and observation instead of triangulation system is adopted to avoid shadow and occlusion. In the projecting system, sinusoidal grating is perpendicular to optical axis.

k-NS: a classifier by the distance to the nearest subspace.

To improve the classification performance of k-NN, this paper presents a classifier, called k -NS, based on the Euclidian distances from a query sample to the nearest subspaces. Each nearest subspace is spanned by k nearest samples of a same class. A simple discriminant is derived to calculate the distances due to the geometric meaning of the Grammian, and the calculation stability of the discriminant is guaranteed by embedding Tikhonov regularization.

On the almost periodic solution of cellular neural networks with distributed delays.

By exponential dichotomy about differential equations, a formal almost periodic solution (APS) of a class of cellular neural networks (CNNs) with distributed delays is obtained. Then, within different normed spaces, several sufficient conditions guaranteeing the existence and uniqueness of an APS are proposed using two fixed-point theorems. Based on the continuity property and some inequality techniques, two theorems insuring the global stability of the unique APS are given.

A functional neural network computing some eigenvalues and eigenvectors of a special real matrix.

How to quickly compute eigenvalues and eigenvectors of a matrix, especially, a general real matrix, is significant in engineering. Since neural network runs in asynchronous and concurrent manner, and can achieve high rapidity, this paper designs a concise functional neural network (FNN) to extract some eigenvalues and eigenvectors of a special real matrix. After equivalent transforming the FNN into a complex differential equation and obtaining the analytic solution, the convergence properties of the FNN are analyzed.