Wrapping Phase Research Articles

Denoising of Wrapped Phase in Digital Speckle Shearography Based on Convolutional Neural Network

Speckle-shearing technology is widely used in defect detection due to its high precision and non-contact characteristics. However, the wrapped-phase recording defect information is often accompanied by a lot of speckle noise, which affects the evaluation of defect information. To solve the problems of traditional denoising algorithms in suppressing speckle noise and preserving the texture features of wrapped phases, this study proposes a speckle denoising algorithm called a speckle denoising convolutional neural network (SDCNN). The proposed method reduces the loss of texture information and the blurring of details in the denoising process by optimizing the loss function. Different from the previous simple assumption that the speckle noise is multiplicative, this study proposes a more realistic wrapped image-simulation method, which has better training results. Compared with representative algorithms such as BM3D, SDCNN can handle a wider range of speckle noise and has a better denoising effect. Simulated and real speckle-noise images are used to evaluate the denoising effect of SDCNN. The results show that SDCNN can effectively reduce the speckle noise of the speckle-shear wrapping phase and retain better texture details.

A two-phase unwrapping three-dimensional measurement method based on phase shift coding

The periodic misalignment of the wrapping phase and fringe order based on the phase-shift + coding method is known to occur easily, leading to jump errors in absolute phase recovery. This paper proposes a two-phase unwrapping method based on shifting-phase coding. This approach encodes the fringe order information and the wrapping phase into the four-step phase shift mode. It generates four composite fringe patterns to directly resolve the wrapping phase and fringe order, thus improving the measurement speed by reducing the number of fringe projections. The interleaved wrapping phases are then constructed by a sequence that changes the initial wrapping phase. The two-phase unwrapping becomes the absolute phase, so the phase error is avoided and the measurement accuracy is improved. The proposed strategy’s effectiveness is proved by the comparison experiments of different methods. Similarly, the suggested method’s reliability is verified by the measurement experiments of several discontinuous objects.

Method of unwrapped phase error correction in phase-measuring profilometry based on adaptive local window

Phase measurement profilometry (PMP) is widely used to reconstruct three-dimensional (3D) shape of objects. However, errors are introduced during standard phase unwrapping process, which considerably brings down measurement accuracy and lowers method reliability. This paper proposes a novel method to identify the errors, and subsequently correct these errors through erecting adaptive local windows. Under proposal, two robust algorithms are developed to correct these errors in initial rudimentary correction as well as in subsequent refined error removal. The adaptive local window is designed as such that the fringe order corresponding to each period of the wrapping phase is independent of each other. Therefore, it avoids the error transmission in the correction process. Experiments are carried out on absolute phase error correction and 3D point cloud reconstruction. Experimental results verified the developed method in terms of reducing the stratification of 3D point cloud and improving the quality of 3D measurement.

Role of particle local curvature in cellular wrapping.

Cellular uptake through membranes plays an important role in adsorbing nutrients and fighting infection and can be used for nanomedicine developments. Endocytosis is one of the pathways of cellular uptake which associate with elastic deformation of the membrane wrapping around the foreign particle. The deformability of the membrane is strongly regulated by the presence of a cortical cytoskeleton placed underneath the membrane. It is shown that shape and orientation of the particles influence on their internalization. Here, we study the role of particle local curvature in cellular uptake by investigating the wrapping of an elastic membrane around a long cylindrical object with an elliptical cross-section. The membrane itself is adhered to a substrate mimicking the cytoskeleton. Membrane wrapping proceeds differently whether the initial contact occurs at the target's highly curved part (vertical) or along its long side (horizontal). We obtain a wrapping phase diagram as a function of the membrane-cytoskeleton and the membrane-target adhesion energy, which includes three distinct regimes (unwrapped, partially wrapped and fully wrapped), separated by two phase transitions. We also provide analytical expressions for the boundaries between the different regimes which confirm that the transitions strongly depend on the orientation and aspect ratio of the nanowire.

Force-induced wrapping phase transition in activated cellular uptake

Intracellular pathogens, including all viruses and many bacteria, enter a host cell through either passive endocytosis or active self-propulsion. Though the cellular uptake of passive particles via endocytic process has been studied extensively, little work has been done on the active entry of self-propelled pathogens, such as Listeria monocytogenes. Here, we present a theoretical model to investigate the adhesive wrapping of a self-propelled particle by a plasma membrane, and find a type of first-order wrapping transition from a small partial wrapping state to a large partial wrapping state triggered by the active force. The phase diagram displays more complex behaviors compared with the passive wrapping mediated merely by adhesion. We also find that a tubular protrusion can be formed if the active force exceeds a force barrier. These results may provide a useful guidance to the study of activity-driven cellular entry of active particles into cells.

A hybrid feature selection approach for Microarray datasets using graph theoretic-based method

The feature selection process plays an important role in different fields, particularly in bioinformatics and microarray gene expression data analysis, for choosing discriminative genes from high-dimensional datasets and selecting a subset of highly relevant features with low redundancy that may lead to build improved prediction models. Consequently, this study proposes a new feature selection method that integrates Preordonnances theory in terms of new Relevance and Complementarity criteria introduced here and also connectivity in undirected Weighted Graphs (PCRWG). The method can handle high-dimensional data. PCRWG retains the relevant and complementary features to select effective features in large scale gene datasets. The proposed algorithm operates in two phases: filtering and wrapping. The strength of the first phase is that it is preceded by a step that further reduces the number of predictors by removing those in disagreement with the target based on the new proposed relevance criterion. Then, the proposed heuristic uses the relevance-complementarity ratio between preordonnances to automatically update the compromise rule between relevance and complementarity. In the wrapping phase, the suggested graph-based approach using maximal clique is based on a powerful relevance-complementarity matrix to consolidate edges, two connected interdependent features are complementary to each other, and it is possible to have high discriminative power when they serve as a group. We highlight the fact that existing graph-based feature selection algorithms do not consider relevance and complementarity simultaneously. The experiments were carried out on three simulated scenarios and the thirteen most popular cancer microarray gene datasets. Formally, they are eight binary and five multi-class microarray data. A 10-fold cross validation was used to evaluate the Support Vector Machine (SVM), Naive Bayes (NB) and artificial Neural Network (NN) classifiers. The empirical results demonstrate the high performance of the proposed hybrid approach when compared to the most recently published articles.

Quantitative phase imaging using a combination of flat fielding and windowed Fourier filtering demodulated by a graph cuts algorithm for screening opaque and transparent objects

In this paper, quantitative phases of opaque and transparent objects are measured precisely using a combination of flat fielding and windowed Fourier filtering demodulated by graph cuts algorithm. The modulated interferogram is corrected first by flat fielding to remove dust particles and adjust inhomogeneity of the interferogram intensity. The corrected interferogram is then convolved by windowed Fourier filtering to produce an interferogram free from speckle noise. The obtained interferogram is reconstructed by the Fourier transform method, or the wavelet method, or the angular spectrum method to extract the wrapping phase of the object. A graph cuts algorithm is used to unwrap the wrapping phase to remove the 2π ambiguity. Experimental results show that quantitative phases of the objects being screened are precisely measured by the proposed method. Moreover, the lateral resolution, which is represented by slope of the roll-off is slightly improved without applications of digital filters. Furthermore, shapes of the echinocytes of the cancerous blood cells which have the sharpest spatial features are seen clearly by the proposed method.

Wrapping of Microparticles by Floppy Lipid Vesicles.

Lipid membranes, the barrier defining living cells and many of their subcompartments, bind to a wide variety of nano- and micrometer sized objects. In the presence of strong adhesive forces, membranes can strongly deform and wrap the particles, an essential step in crossing the membrane for a variety of healthy and disease-related processes. A large body of theoretical and numerical work has focused on identifying the physical properties that underly wrapping. Using a model system of micron-sized colloidal particles and giant unilamellar lipid vesicles with tunable adhesive forces, we measure a wrapping phase diagram and make quantitative comparisons to theoretical models. Our data are consistent with a model of membrane-particle interactions accounting for the adhesive energy per unit area, membrane bending rigidity, particle size, and vesicle radius.

An Algorithm Combining the Branch-Cut Method and Rhombus Phase Unwrapping Algorithm

In order to solve the problem that the wrapped phase cannot be unwrapped effectively, this paper proposes an algorithm combining the branch-cut method and rhombus phase unwrapping algorithm for unwrap phase. The algorithm combines the advantages of the two methods, at first, the Goldstein’s branch-cut method is used to find the residual points in the wrapping phase, then the rhombus phase unwrapping algorithm is used to unwrap the non-residual area phase, finally the phase of the residual area is obtained by using the cubic spline interpolation method, so as to ensure that the phase of each pixel can be unwrapped smoothly. The simulation results verify the feasibility and effectiveness of the algorithm, the combined phase unwrapping algorithm can effectively reduce the line and island phenomenon.

Robust composite sine-trapezoidal phase-shifting algorithm for nonlinear intensity

Phase-shifting profilometry (PSP) is an efficient three-dimensional (3D) measurement technique. The accuracy of the PSP based 3D measurement system is generally affected by the nonlinear intensity response of the projector. In general, nonlinear errors can be reduced by projecting more fringe patterns, but this approach significantly increases the measuring time. To improve the measurement accuracy and speed, a new composite sine-trapezoidal phase-shifting profilometry technique that suppresses nonlinear error is proposed in this study. The main idea of the proposed method is projecting a set of sinusoidal fringe patterns and two sets of composite sine-trapezoidal fringe patterns with different intensity ranges in sequence. Among these patterns, the sinusoidal fringe is used to encode the pixel position, and the order of the wrapping phase is determined by the region code of the composite fringe. The flat intensity parts of the composite fringe are used to encode the nonlinear intensity response of the measurement system. Through system identification, the response parameters of the pixels can be obtained, and the gray value of the pixels can be corrected to gain a better phase distribution. Compared to the straight edges of the trapezoidal fringe, the sinusoidal edges of the composite fringe are more robust to the projector defocus, which improves the accuracy of the identification of the system intensity response model. Simulation results show that the proposed method can achieve a higher phase retrieval accuracy than the trapezoidal PSP and the sine PSP. The experiment results indicate that the proposed method has a better measurement accuracy than those of the standard 3-step and 4-step sine PSP methods.

Structures and mechanical behaviors of soft nanotubes confining adhesive single or multiple elastic nanoparticles

The mechanical interaction between soft membrane nanotubes and confined nanoparticles plays essential roles in numerous cellular activities such as inter- and intracellular transport and signaling, membrane tubulation and pearling transformation driven by adsorbed nanocomponents, and cooperative cell uptake. The aim of this work is to theoretically analyze how structures and mechanical behaviors of particle-in-tube complexes are regulated by the particle elasticity, covering the cases of a single particle and multiple particles of spatial periodicity. Depending on the interplay of membrane tension, adhesion energy, nanoparticle size, and the rigidity ratio between the nanoparticles and soft tubes, characteristic interaction states in terms of wrapping degrees of the confined particles have been identified, and the corresponding wrapping phase diagrams are determined. As the particles become softer, the adhesion energy required for the partial-wrapping state becomes lower but that for full-wrapping is larger, and a rich variety of characteristic system configurations are observed including the bulged or undulated cylinders, necklaces of spheres or prolates, and sausage-string shapes. Moreover, two fundamental modes of interaction between multiple nanoparticles and the soft nanotube are revealed, the cooperative wrapping and individual wrapping. Based on perturbation analysis at small tube deformation and configurational assumptions of catenoid, torus, and one-sheeted hyperboloid at more general tube deformation, we have also obtained analytical solutions on the wrapping of rigid spherical nanoparticles in soft tubes. Comparison between wrapping of elastic particles by a flat membrane and by a soft membrane tube is discussed. This study enriches our knowledge on the diversity of membrane tubules in morphologies and structures.

An Efficient hybrid filter-wrapper metaheuristic-based gene selection method for high dimensional datasets

Feature selection problem is one of the most significant issues in data classification. The purpose of feature selection is selection of the least number of features in order to increase accuracy and decrease the cost of data classification. In recent years, due to appearance of high-dimensional datasets with low number of samples, classification models have encountered over-fitting problem. Therefore, the need for feature selection methods that are used to remove the extensions and irrelevant features is felt. Recently, although, various methods have been proposed for selecting the optimal subset of features with high precision, these methods have encountered some problems such as instability, high convergence time, selection of a semi-optimal solution as the final result. In other words, they have not been able to fully extract the effective features. In this paper, a hybrid method based on the IWSSr method and Shuffled Frog Leaping Algorithm (SFLA) is proposed to select effective features in a large-scale gene dataset. The proposed algorithm is implemented in two phases: filtering and wrapping. In the filter phase, the Relief method is used for weighting features. Then, in the wrapping phase, by using the SFLA and the IWSSr algorithms, the search for effective features in a feature-rich area is performed. The proposed method is evaluated by using some standard gene expression datasets. The experimental results approve that the proposed approach in comparison to similar methods, has been achieved a more compact set of features along with high accuracy. The source code and testing datasets are available at https://github.com/jimy2020/SFLA_IWSSr-Feature-Selection.

Wrapping of a nanowire by a supported lipid membrane

Internalization of particles by cells plays a crucial role for adsorbing nutrients and fighting infection. Endocytosis is one of the most important mechanisms of particle uptake, which encompasses multiple pathways. Although endocytosis is a complex mechanism involving biochemical signaling and active force generation, the energetic cost associated with the large deformations of the cell membrane wrapping around a foreign particle is an important factor controlling this process, which can be studied using quantitative physical models. Of particular interest is the competition between membrane-cytoskeleton and membrane-target adhesion. This competitive adhesion mechanism can be reproduced to some extent by studying particle wrapping by a membrane adhered to a substrate. We propose a theoretical analysis of this process. Here, we explore the wrapping of a lipid membrane around a long cylindrical object in the presence of a substrate mimicking the cytoskeleton. Using discretization of the Helfrich elastic energy, which accounts for the membrane bending rigidity and surface tension, we obtain a wrapping phase diagram as a function of the membrane-cytoskeleton and the membrane-target adhesion energy, which includes unwrapped, partially wrapped and fully wrapped states. We provide an analytical expression for the boundary between the different regimes. While the transition to partial wrapping is independent of the membrane tension, the transition to full wrapping is very much influenced by the membrane tension. We also show that target wrapping may proceed in an asymmetric fashion in the full wrapping regime.

An optimized phase-shifting algorithm for depth image acquisition

As developing science and technology, traditional two-dimensional computer vision cannot meet the people’s needs for the three-dimensional recognition, and the depth information of objects are required by more and more applications. Recently, structured light has become one of the core techniques of depth acquisition. The main idea of the approach is first projecting pre-designed pattern onto objects, then capturing an image and processing further. In a structured light system, the phase-shifting algorithm, which is a depth acquiring algorithm for sinusoidal pattern, is discussed in this paper, and is argued that its performance weakness for applying in the real time environments. Then we propose the performance optimization on its phase wrapping step and phase unwrapping step, respectively. Finally, we compare acquiring results and advantages as well as disadvantages of them by experiments results. Experiments show that the optimized phase-shifting algorithm is 4.6× faster than the original one with the ignorable errors.

Field inhomogeneity correction using partial differential phases in magnetic resonance imaging

Correction of an inhomogeneous magnetic field is proposed using partial differential phases in magnetic resonance imaging. Estimation of the inhomogeneous magnetic field from a measured phase is not an easy task due to phase wrapping and chemical-dependent phase shifts. Using the proposed partial differential phase technique, such problems are resolved. The proposed technique uses most of the 3D pixel data regardless of chemical compounds for the estimation of the inhomogeneous magnetic field. A large number of partial difference data compared to the number of expansion terms for the model of inhomogeneous magnetic field provides a very stable estimation, robust to noise. The technique is applicable to in vivo shimming, water-fat imaging, eddy current compensation, and most phase-related measurements and imaging. The efficacy of the proposed technique is demonstrated with in vivo water-fat imaging.

InSAR Phase Unwrapping Method Using Quality Map with Residues

We propose an algorithm which optimizes the way of choosing quality threshold according to the distribution of the residues.All the pixels of the wrapping phase figure are classified by this threshold into two types,high quality pixels and low quality pixels,and given different weights and then unwrapped.Experimental results show that the proposed algorithm has the advantages of less information loss,clearer guide line,fewer manual works,and its precision is superior to some of the conventional algorithms such as the branch-cut algorithm.

Phase Error Immunity Optical Measurement System

In this paper, a 3D shape measurement system based on a digital fringe projection and phase-shifting technique was described. The fringe generated by a computer was projected onto the object by utilizing a digital video projector. Four steps phase shifting algorithm was used to calculate the 2π wrapping phase, and a heterodyne principle was used to unwrap the phase to obtain the real phase. Then the 3D coordinates object was calculated base on stereo vision algorithm by using the real phase as a constraint. Experiments demonstrated that the system was not sensitive to the phase error caused by the DVP’s nonlinearity.