Multi-scale Textures Research Articles

An Enhance Relative Total Variation With BF Model for Edge-Preserving Image Smoothing

In image processing, edge-preserving image smoothing methods that maintain weak structure while smoothing multiscale textures with strong gradients remain challenging. In this paper, a new global optimization method named Enhance Relative Total Variation Embedded with Bilateral Filtering is proposed. The texture and structure are intuitively considered separately from the model generalization. First, the weak structures are separated from the over-penalized texture and structure items by embedding bilateral filtering. Then, the in-set shrinking edges/structures are gradually recovered by constructing a contrast stretching function. In comparison to current state-of-the-art methods, experimental results demonstrate that the method is effective in maintaining weak structures and suppressing multiscale textures.

Improved estimation of aboveground biomass of regional coniferous forests integrating UAV-LiDAR strip data, Sentinel-1 and Sentinel-2 imageries

BackgroundForest aboveground biomass (AGB) is not only the basis for estimating forest carbon storage, but also an important parameter for evaluating forest carbon cycle contribution and forest ecological function. Data saturation and fewer field plots limit the accuracy of AGB estimation. In response to these questions, we constructed a point-line-polygon framework for regional coniferous forests AGB mapping using field survey data, UAV-LiDAR strip data, Sentinel-1 and Sentinel-2 imageries in this study. Under this framework, we explored the feasibility of acquiring the LiDAR sampling plots using the LiDAR sampling strategy consistent with the field survey, and analyzed the potentials of multi-scale wavelet transform (WT) textures and tree species stratification for improving AGB estimation accuracy of coniferous forests in North China.ResultsThe results showed that UAV-LiDAR strip data of high density point clouds could be used as a sampling tool to achieve sample amplification. Experimental comparison results showed that the Sentinel-based AGB estimation models incorporating the multi-scale WT textures and SAR data performed better, and the model based on coniferous forests tree species significantly improved the performance of AGB estimation. Additionally, the accuracy comparison using different validation sets indicated that the proposed LiDAR sampling strategy under the point-line-polygon framework was suitable for estimating coniferous forests AGB on a large area. The highest accuracy of AGB estimation of larch, Chinese pine and all coniferous forests was 74.55%, 78.96%, and 73.42%, respectively.ConclusionsThe proposed approach can successfully alleviate the data signal saturation issue and accurately produce a large-scale wall-to-wall high-resolution AGB map by integrating optical and SAR data with a relative small number of field plots.

Combining multi-scale surface texturing and DLC coatings for improved tribological performance of 3D printed polymers

Polymer components fabricated by additive manufacturing typically show only moderate strength and low temperature stability, possibly leading to severe wear and short lifetimes especially under dry tribological sliding. To tackle these shortcomings, we investigated the combination of single- and multi-scale textures directly fabricated by digital light processing with amorphous diamond-like carbon (DLC) coatings. The topography of the samples and conformity of the coatings on the textures are assessed and their tribological behaviour under dry conditions is studied. We demonstrate that the surface textures have a detrimental tribological effect on the uncoated samples. This changes with the application of DLC coatings since friction substantially reduces and wear of the textures is not observed anymore. These trends are attributed to the protection of the underlying polymer substrate by the coatings and a reduced contact area. The best tribological performance is found for a coating with highest hardness and hardness-to-elasticity ratios. Moreover, multi-scale textures perform slightly better than single-scale textures due to a smaller real contact area. Summarizing, we verified that the high flexibility and low production costs of 3D printing combined with the excellent mechanical and tribological properties of DLC results in synergistic effects with an excellent performance under dry sliding conditions.

Multi-Scale Femtosecond-Laser Texturing for Photothermal Efficiency Enhancement on Solar Absorbers Based on TaB2 Ceramics.

Tantalum boride is an ultra-refractory and ultra-hard ceramic known so far for its favorable high-temperature thermo-mechanical properties and also characterized by a low spectral emittance, making it interesting for novel high-temperature solar absorbers for Concentrating Solar Power. In this work, we investigated two types of TaB2 sintered products with different porosities, and on each of them, we realized four femtosecond laser treatments differing in the accumulated laser fluence. The treated surfaces were then characterized by SEM-EDS, roughness analysis, and optical spectrometry. We show that, depending on laser processing parameters, the multi-scale surface textures produced by femtosecond laser machining can greatly increase the solar absorptance, while the spectral emittance increase is significantly lower. These combined effects result in increased photothermal efficiency of the absorber, with interesting perspectives for the application of these ceramics in Concentrating Solar Power and Concentrating Solar Thermal. To the best of our knowledge, this is the first demonstration of successful photothermal efficiency enhancement of ultra-hard ceramics using laser machining.

Impact of multi-scaled surface textures on tribological performance of parallel sliding contact under lubricated condition

Single-scale textured sliding contacts are effective for low-load and high-speed operating conditions. However, most machine-driven components operate under dynamic load and speed conditions. Therefore, to effectively counteract the dynamic operating conditions, the idea of multi-scale textures is devised. The present work describes the fabrication of multi-scale textured surfaces through Nd: YAG laser. Also, the effect of area density and texture depth of multi-scale textured surfaces on tribological parameters like frictional coefficient and wear rate was analyzed through a Pin-on-disc test rig. The results depict that in most loading conditions, the area density of 0.3 and the texture depth of 10 µm exhibit better tribological properties. Moreover, the obtained experimental results are in-line with numerical results.

Numerical Analysis of Friction Reduction and ATSLB Capacity of Lubricated MTS with Textured Micro-Elements

The simulation analysis numerically investigates the thermoelastic lubricated interfacial Textured Micro-Element (TME) load-bearing contact, a theoretical model is proposed, and the effective friction reduction and Anti-Thermoelastic Scuffing Load bearing (ATSLB) capacity between random rough Meshing Teeth Surfaces (MTS) are presented, the mechanism linking interfacial thermoelastic lubrication, TME meshing friction reduction and ATSLB is revealed. The real contact domain area between MTS with multi-scale Micro-Element Textures (MET) is obtained for the numerical calculation of the three-dimensional equivalent TME contact volume, which is the correlation bridge between friction reduction and ATSLB of the thermoelastic lubrication interface. The proposed theoretical model predicts the time-varying behaviour of the textured meshing interface friction reduction with TME contact load under thermoelastic lubrication conditions. Numerical simulations show that the textured interface meshing volume is the key to solving the load-bearing problem of line contact between randomly rough teeth surfaces. The friction coefficients of the MTS are reduced by 13–24%. The lubricated load-bearing and friction reduction behaviour between the textured MTS is quantified by the thermoelastic voids of TME interface and actual meshing volume ratio, which provides a new perspective for further insight into the lubrication and friction reduction behaviour between the MTS with multi-scale MET-ATSLB coupling mechanism.

Numerical simulation on hydrodynamic lubrication performance of bionic multi-scale composite textures inspired by surface patterns of subcrenata and clam shells

Inspired by the surface of shells in natural world, six bionic multi-scale composite surface textures were designed. Numerical simulation was carried out to investigate the mechanism for influences of the texture geometric parameters and service conditions on lubrication characteristics. The results revealed that the bionic surface enhanced the load-carrying capacity and reduced the friction coefficient. Groove combinations offered a better lubrication performance than wavy combinations. The texture geometric parameters showed significant influence on the frictional characteristics. For all texture shapes, the load-carrying capacity increased approximately linearly with the rotational speed and decreased with the water film thickness. The results would help guide effective texture design to improve the lubrication performance of axial piston pump friction pairs under marine environments.

JNMR: Joint Non-linear Motion Regression for Video Frame Interpolation.

Video frame interpolation (VFI) aims to generate predictive frames by motion-warping from bidirectional references. Most examples of VFI utilize spatiotemporal semantic information to realize motion estimation and interpolation. However, due to variable acceleration, irregular movement trajectories, and camera movement in real-world cases, they can not be sufficient to deal with non-linear middle frame estimation. In this paper, we present a reformulation of the VFI as a joint non-linear motion regression (JNMR) strategy to model the complicated inter-frame motions. Specifically, the motion trajectory between the target frame and multiple reference frames is regressed by a temporal concatenation of multi-stage quadratic models. Then, a comprehensive joint distribution is constructed to connect all temporal motions. Moreover, to reserve more contextual details for joint regression, the feature learning network is devised to explore clarified feature expressions with dense skip-connection. Later, a coarse-to-fine synthesis enhancement module is utilized to learn visual dynamics at different resolutions with multi-scale textures. The experimental VFI results show the effectiveness and significant improvement of joint motion regression over the state-of-the-art methods.

CO3Net: Coordinate-Aware Contrastive Competitive Neural Network for Palmprint Recognition

Palmprint recognition achieves high discrimination for identity verification. Compared with handcrafted local texture descriptors, convolutional neural networks (CNNs) can spontaneously learn optimal discriminative features without any prior knowledge. To further enhance the features’ representation and discrimination, we propose a coordinate-aware contrastive competitive neural network (CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Net) for palmprint recognition. To extract the multi-scale textures, CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Net consists of three parallel learnable Gabor filters (LGF)-based texture extraction branches that learn the discriminative and robust ordering features. Due to the heterogeneity of palmprints, the effects of different textures on the final recognition performance are inconsistent, and dynamically focusing on the textures is beneficial to the performance improvement. Then, CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Net introduces the attention modules to explore the spatial information, and selects more robust and discriminative textures. Specifically, coordinate attention is embedded into CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Net to adaptively focus on the important textures from the positional information. Since it is difficult for the cross-entropy loss to build a compact intra-class and separate inter-class feature space, the contrastive loss is employed to jointly optimize the network. CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Net is validated on four public datasets, and the results demonstrate the remarkable recognition performance of the proposed CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Net compared to other state-of-the-art methods.

Facial Makeup Detection Using Multi-Scale Local Binary Patterns and Convolutional Neural Network Fusion

This study presents a novel facial makeup detection scheme using fusion of multi-scale Local Binary Patterns (ms-LBP) texture methods and convolutional neural network (CNN) deep learning extractor. Facial makeups affect the accuracy of face recognition systems due to appearance alteration of individuals. In order to fuse the facial features, the proposed scheme first considers concatenation of extracted global histogram of a whole image using three different LBP operators. The LBP scales are used to extract and then concatenate the local histogram of overlapping and nonoverlapping blocks of images. This way utilizes the advantages of microtexton information of local primitives besides the extracted global textures. Finally, the extracted features using CNN feature extractor are combined with global and local multi-scale textures. In general, CNN deep learning extractor learns high-level discriminative characteristics of images and therefore the proposed system improves the facial makeup detection rate by involving both microtexton and discriminative information of facial images. In addition, the proposed method attempts to select the optimized subset of facial features to increase the detection performance of system by applying Particle Swarm Optimization (PSO) technique after feature level fusion. The paper uses Support Vector Machine (SVM) classifier for classifying the facial vectors into makeup or no-makeup classes. The proposed scheme is then evaluated using YMU, VMU and MIW facial makeup databases with consideration of light, medium and heavy makeups on several datasets. Experimental results analysis clarifies the effectiveness of proposed facial makeup detection framework of this study.

Fabrication of Water‐ and Ice‐Repellent Surfaces on Additive‐Manufactured Components Using Laser‐Based Microstructuring Methods

Laser patterning techniques have shown in the last decades to be capable of producing functional surfaces on a large variety of materials. A particular challenge for these techniques is the treatment of additively manufactured parts with high roughness levels. The presented study reports on the surface modification of additive‐manufactured components of Ti64 and Al–Mg–Sc (Scalmalloy), with the aim of implementing water‐ and ice‐repellent properties. Different laser‐based microstructuring techniques, using nanosecond and picosecond pulses, are combined to create multiscale textures with feature sizes between ≈800 nm and 21 μm. The wettability could be set to static water contact angles between 141° and 153° for Ti64 and Al–Mg–Sc, respectively. In addition, surface free energy is analyzed for different surface conditions.

Investigation of novel multiscale textures for the enhancement of the cutting performance of Al2O3/TiC ceramic cutting tools

Currently, the high temperature and severe friction conditions at the tool-chip interface are the main reasons for ceramic tool wear failures. Surface texturing as a geometric extension for cutting tools is a promising way to extend their service life. In this study, a novel type of multiscale texture was developed, and its effect on the cutting performance of an Al2O3/TiC ceramic cutting tool while machining AISI H13 steel was explored in a conventional cooling environment. The cutting force, cutting temperature, and tool wear morphology were investigated at cutting velocities ranging from 80 to 249 m/min. Microgroove textured Al2O3/TiC ceramic tools were prepared for comparison. The results show that the structure of the multiscale textures maintained good integrity over the range of cutting velocities. Thus, the synergistic effect of the microscale and nanoscale textures promoted the introduction and permeation of the cutting fluid. Therefore, the multiscale textures effectively enhanced the cutting performance of the Al2O3/TiC ceramic tools.

Tribological study of multi-scale textured parallel sliding contacts by considering a mixed lubrication regime and mass conservation condition

Surface texturing is a viable technique to enhance the tribological performance of sliding interacting contacts. Single-scaled surface textures exhibit better tribological performance only at hydrodynamic lubrication regime (fluid film pressure) but not in mixed lubrication regime where fluid film pressure and asperity contact pressure co-exists. In most of the machinery with the increase in load and/or decrease in speed, there is a shift of lubrication regime from hydrodynamic to mixed lubrication. To address this, the present work proposed a multi-scale (a combination of shallow and deep) textures concept. A numerical model is developed to study its effect on the tribological characteristics of parallel sliding contacts by considering mixed lubrication regime and mass-conservative cavitation condition. It has been observed that multi-scaled textures exhibit superior results in comparison with single-scaled textures. Moreover, improved tribological characteristics are observed when shallow surface textures are placed first towards the fluid inlet flow.

Tribological performance of UV picosecond laser multi-scale composite textures for C/SiC mechanical seals: Theoretical analysis and experimental verification

The performance of mechanical seals can be improved by using multi-scale composite textures with spiral grooves and micro-dimples, but without a clear texture function mechanism, it is difficult to optimize the textures on the sealing surface. This research established a mathematical model based on the mass-conservative JFO cavitation boundary to analyze the mechanical seal performance of multi-scale composite micro-textures. We use the multi-grid method for numerical solutions, investigate the hydrodynamic lubrication characteristics of composite-textured mechanical seals, and inspect the coupling effect of micro-dimples and spiral grooves. We also analyzed the influence of the geometrical parameters of composite textures on the sealing performance and optimized the sealing surface textures using theoretical analysis. The numerical analysis showed that the multi-scale composite-textured mechanical seal produced a coupling effect between the micro-dimples and spiral grooves, which improved the lubrication of the sealing pair. Eventually, C/SiC mechanical seal bench tests confirmed the tribological improvement of composite textures compared with the un-textured seal under various sealing liquid pressures and rotation speeds. Through comparing the Stribeck curve, the multi-scale composite textured C/SiC mechanical seals have a lower and more stable friction torque than the un-textured.

Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis

Rapid and accurate crop aboveground biomass estimation is beneficial for high-throughput phenotyping and site-specific field management. This study explored the utility of high-definition digital images acquired by a low-flying unmanned aerial vehicle (UAV) and ground-based hyperspectral data for improved estimates of winter wheat biomass. To extract fine textures for characterizing the variations in winter wheat canopy structure during growing seasons, we proposed a multiscale texture extraction method (Multiscale_Gabor_GLCM) that took advantages of multiscale Gabor transformation and gray-level co-occurrency matrix (GLCM) analysis. Narrowband normalized difference vegetation indices (NDVIs) involving all possible two-band combinations and continuum removal of red-edge spectra (SpeCR) were also extracted for biomass estimation. Subsequently, non-parametric linear (i.e., partial least squares regression, PLSR) and nonlinear regression (i.e., least squares support vector machine, LSSVM) analyses were conducted using the extracted spectral features, multiscale textural features and combinations thereof. The visualization technique of LSSVM was utilized to select the multiscale textures that contributed most to the biomass estimation for the first time. Compared with the best-performing NDVI (1193, 1222 nm), the SpeCR yielded higher coefficient of determination (R2), lower root mean square error (RMSE), and lower mean absolute error (MAE) for winter wheat biomass estimation and significantly alleviated the saturation problem after biomass exceeded 800 g/m2. The predictive performance of the PLSR and LSSVM regression models based on SpeCR decreased with increasing bandwidths, especially at bandwidths larger than 11 nm. Both the PLSR and LSSVM regression models based on the multiscale textures produced higher accuracies than those based on the single-scale GLCM-based textures. According to the evaluation of variable importance, the texture metrics “Mean” from different scales were determined as the most influential to winter wheat biomass. Using just 10 multiscale textures largely improved predictive performance over using all textures and achieved an accuracy comparable with using SpeCR. The LSSVM regression model based on the combination of the selected multiscale textures, and SpeCR with a bandwidth of 9 nm produced the highest estimation accuracy with R2val = 0.87, RMSEval = 119.76 g/m2, and MAEval = 91.61 g/m2. However, the combination did not significantly improve the estimation accuracy, compared to the use of SpeCR or multiscale textures only. The accuracy of the biomass predicted by the LSSVM regression models was higher than the results of the PLSR models, which demonstrated LSSVM was a potential candidate to characterize winter wheat biomass during multiple growth stages. The study suggests that multiscale textures derived from high-definition UAV-based digital images are competitive with hyperspectral features in predicting winter wheat biomass.

Stable Superhydrophobic Aluminum Surfaces Based on Laser-Fabricated Hierarchical Textures

Laser-microtextured surfaces have gained an increasing interest due to their enormous spectrum of applications and industrial scalability. Direct laser interference patterning (DLIP) and the well-established direct laser writing (DLW) methods are suitable as a powerful combination for the fabrication of single (DLW or DLIP) and multi-scale (DLW+DLIP) textures. In this work, four-beam DLIP and DLW were used independently and combined to produce functional textures on aluminum. The influence of the laser processing parameters, such as the applied laser fluence and the number of pulses, on the resulting topography was analyzed by confocal microscopy and scanning electron microscopy. The static long-term and dynamic wettability characteristics of the laser-textured surfaces were determined through water contact angle and hysteresis measurements, revealing superhydrophobic properties with static contact angles up to 163° and hysteresis as low as 9°. The classical Cassie–Baxter and Wenzel models were applied, permitting a deeper understanding of the observed wetting behaviors. Finally, mechanical stability tests revealed that the DLW elements in the multi-scale structure protects the smaller DLIP features under tribological conditions.

Facile fabrication of multi-scale microgroove textures on Ti-based surface by coupling the re-solidification bulges derived from nanosecond laser irradiation

A facile method is proposed to fabricate multi-scale microgroove textures (MGT) on Ti-based implant material. The effect of some primary parameters on the morphology of laser-irradiated surface is studied in detail. The results show that at the scanning speed of 42 mm/s, when the laser intensity is ≥4.0 J/cm2 typical microgroove structure with raised edges is produced by single-line scanning irradiation, and various MGT can be easily fabricated by multi-line scanning irradiation through properly adjusting the scanning interval in accordance with the feature size of microgroove structures. Meanwhile, it is firstly found that multi-scale MGT perpendicular to the scanning direction can be achieved by applying suitable high scanning speed with small scanning interval. Using a laser spot with the diameter of 20 μm, multi-scale MGT with various periods ranging from 15 (less than the laser spot diameter) to 35 μm are successfully fabricated on TA2 surfaces, and perpendicular ones with the same period of 21 μm but different ridge widths are also obtained. The aspect ratio of these MGT is >1.5. Wettability measurements reveal that the water droplet on the produced multi-scale MGT owns anisotropic spreading speeds or spreading lengths. The energy dispersive spectroscopy tests indicate that their ridges are rich in titanium oxides which are good hemo-compatibility materials. The proposed method has a promising application for fabricating multi-scale MGT like those on natural vessel to the stent surface to improve its hemo-compatibility.

Single-step meso/micro scale hierarchical surface-structuring using an innovative tool-based hybrid laser-electrochemical micromachining process

In recent years, fabrication of varied topographies, hierarchical structures and multiscale textures has attracted the research interest. Single step fabrication of hierarchical structures at sub-mm or microscale still remains a challenge for machining processes. In this work, a novel tool based hybrid laser-electrochemical micromachining process is presented which combines laser and electrochemical process energies on same machining axis by means of a hybrid tool. The hybrid tool acts as both an ECM tool and a multimode waveguide for the laser. With this technology, generation of uniform dual-scale surface structures is realized in single step. These structures have a dual-scale hierarchical morphology. Array of hierarchical cavities have been fabricated by this process on Inconel IN718 using a 1.2 mm diameter tool. Process details have been presented and machined hierarchical cavities are analyzed. It is shown that by controlling the process parameters of both laser and ECM, the geometry and structure of these hierarchical cavities can be controlled. Overall, it is shown that the hybrid laser-electrochemical micromachining technology facilitates fabrication of hierarchical surface structures due to application of different process energies at different scales. Further scaling down of the features is possible with scaling down of hybrid tool dimensions using state of the art microfabrication techniques.

Multilevel self-organization on anodized aluminium: discovering hierarchical honeycomb structures from nanometre to sub-millimetre scale.

Herein, the one-step spontaneous formation of multiscale hierarchical honeycomb-like textures on anodized aluminium is reported for the first time. A self-organized array of unprecedentedly large sub-millimetre hemispherical pores was observed on the upper hierarchy level, while superimposed well-ordered hexagonal cells on the lower hierarchy level maintained their classically known nanoscale self-ordering periodicity (50-60 nm). The larger macroscopic cells (0.45-0.90 mm) show great morphological similarity to the conventional nanosized porous anodic structures, but arise on a purely metallic surface without any analytically detectable amount of deposited alumina. The observed cell size expansion (approximately by a factor of 104) during Al anodizing is colossal in comparison with the size variation ranges reported in the literature so far. It reveals a dramatic nonlinearity effect in a dynamic self-organizing system upon a moderate change of experimental parameters (specifically, this work focuses on the effect of anodizing temperature). Such multilevel self-organization in a single aluminium anodizing process sheds new light on the origin of self-ordered anodic structures and allows rethinking the whole range of opinions regarding their formation mechanism that have been proposed to date.

Structure-Aware Texture Filtering Based on Local Histogram Operator

Smoothing the multi-scale textures with strong gradient while maintaining the weak structures is still a challenging work for the existing texture filtering methods. In this paper, we propose a variant of the bilateral filter to address this issue. Unlike the classical bilateral filter, which usually uses a fixed range kernel for texture filtering, the variant uses an adaptive range kernel, that is, if a pixel is located near a structure, the range kernel assigned to the pixel is small, otherwise, the range kernel is large. This mechanism ensures the preservation of sharp edges. In order to perform structure measurement, we propose a local histogram operator, which can identify structures from textures by comparing the difference in color distribution between the left and right half neighboring windows centered on the pixel of interest. In addition, after once bilateral filtering, instead of multiple iterations, a novel anisotropic filter is designed to remove residual textures, which can avoid edge blurring to some extent. Experimental results demonstrate the effectiveness of our method by comparing it with the state-of-the-art methods in preserving weak structures as well as suppressing textures with strong gradient or varying scales.