Texture Part Research Articles

Long-term Performance of Textured HDPE Geomembranes in Municipal Solid Waste Landfills

The effect of texturing (co-extrusion using a blowing agent) on the long-term performance of geomembranes immersed in synthetic municipal solid waste (MSW) leachate is examined over an approximately 8-year period. Antioxidant depletion of the textured part is shown to be much faster than that of the smooth edge. Likewise, the degradation in the tensile break properties at 85°C is faster for the textured part than for the smooth edge portion and smooth equivalent. The updated estimates for antioxidant depletion time based on data at four different temperatures (40, 55, 75, and 85°C) over 98 months of data collection are compared with predictions based on 34 months of data and the implications are discussed. Nominal failure was reached at 75°C, and this combined with data at 85°C allows prediction of the time between depletion of Std-OIT and nominal failure at lower temperatures. The effect of salt concentration in incubation fluid on the time to degradation is examined. Finally, the paper comments on the uses of textured versus smooth geomembrane.

Infrared image deblurring with Framelet regularization in the background of salt-and-pepper noise

Infrared images have been widely employed in many fields. However, the relative motion between a target and an imaging device and the interference of salt-and-pepper noise lead to the degradation of infrared images. Traditional infrared image recovery methods require an image to be restored as a whole, which leads to mutual interference between the cartoon and texture parts of an image in the restoration process. In this study, a morphological component analysis decomposition method was developed. This method decomposes an image into cartoon and texture parts. Meanwhile, the l p pseudo-norm is introduced to characterize the sparsity of the stationary Framelet transform coefficients and noise, and mask matrix is applied to protect parts that were not contaminated. The proposed method was then solved using an alternating-direction multiplier method. The proposed method was compared with more advanced image recovery methods. The results show that the proposed method has a significant performance improvement.

Optical encoding and hiding scheme for a double image based on chaotic fingerprint phase masks and phase-shifting digital holography.

This paper proposes a novel, to the best of our knowledge, double-image hiding scheme based on the chaotic fingerprint phase masks (CFPMs) and three-step phase-shifting digital holography (PSDH). First, the two images to be hidden are encoded into a complex amplitude image, and then with the help of the CFPM located in the Fresnel transform (FrT) domain and the three-step PSDH, the complex amplitude image can be encoded into three noise-like interference holograms. Finally, the three noise-like interference holograms are hidden into the texture part of the host image by the discrete wavelet transform based fusion approach and variational image decomposition technique. This scheme can simultaneously hide two images into one host image, and the invisibility and robustness of the hiding scheme can be well balanced by embedding the secret image in the texture of the host image. Additionally, the introduction of a biometric feature increases the association of the key and the authorized user, and the parameters of the chaotic map and FrT can also provide additional security tothe proposed scheme. We have verified the scheme's feasibility, security, and robustness through extensive experiments.

A Study on the Formation of Fiber Texture in the Subsurface Layer of Hot-Rolled Plate of 3.2%Si Grain-Oriented Steel

During the hot rolling process, inhomogeneity microstructure and variations of texture along the thickness direction of hot-rolled plate were formed through shear deformation and recrystallization. The purpose of this study is to investigate the formation and evolution of fiber texture in hot-rolled plate of grain-oriented silicon steel. The results indicate that, in the surface layer s = 1 of hot-rolled plate, a large number of equiaxed grains are formed under high shear force. Under the surface layer, as shear force gradually decreases along the thickness direction, the grain aspect ratio of recrystallized grains gradually increases along the thickness direction of hot-rolled plate, and the shape of the recrystallized grains formed in the subsurface layer and center layer becomes more irregular along the thickness direction. Under the combination of compressive force and decreasing shear force along the thickness direction, fiber texture composed of dozens of specific orientations (such as {112}<111>, {110}<112>, {213}<364>, {441}<104> and Goss orientations) is formed in the subsurface layer s = 0.5~0.9 and gradually evolves along the thickness direction. Therefore, the formation of Goss texture is a small part of fiber texture and is the result of texture evolution in the subsurface layer s = 0.5~0.9.

Study of Movement Quality Enhancement Strategies in Modern Dance

In the practice of modern dance, many people pay attention to whether the movements are the same as what the teacher has done, whether the rhythm and speed have kept up with the music, and rarely pay attention to the most important part of movement texture, without observing the movement texture shown by the teacher in doing demonstration movements. In modern dance, the performance of movement texture can judge the professionalism of the dancer. How should we train the movement quality of modern dance is the issue to be discussed and studied. In modern dance performance, the accuracy of movement performance and dance texture are inseparable, different dance movement texture, the dance feeling is different, and there is a clear difference in the dance performance with or without movement texture. The main content of this essay is to talk about what kind of importance dance texture has in modern dance, and to explore the strategy of enhancing dance texture in modern dance.

Paleoproterozoic hydration of gabbronorite from the Mesoarchean Lechana layered complex, eastern Botswana

Amphibole in mafic rocks of Archean layered complexes could be magmatic, metamorphic or metasomatic in origin. In this study, we use a combined field, petrography, mineral chemistry, phase equilibria modeling and 40Ar/39Ar geochronology approach to characterize amphibole in gabbronorites from the Mesoarchean Lechana layered complex in eastern Botswana. To get a better understanding of the overprint events, the characteristics of amphibole in the spatially associated amphibolite are also included. The gabbronorite preserves magmatic cumulate texture, with brownish amphibole replacing pyroxene as widespread thin (up to 80 μm) rims. In comparison, the greenish amphibole in amphibolite form part of granoblastic polygonal texture. Amphibolite preserve evidence for anatectic and metasomatic overprints, while the gabbronorite only exhibit effects of the later. Phase equilibria modeling provide P-T conditions and H2O content for the amphibole formation in the gabbronorite. The 40Ar/39Ar geochronology gave an age of 2037 ± 27 Ma for this process. We relate the Paleoproterozoic hydration to form amphibole in gabbronorite to the residual aqueous fluids expelled during crystallization of an anatectic melt in the spatially associated amphibolite. The results highlight an example of the formation of amphibole in Archean layered mafic rocks via a post-magmatic hydration event.

An edge map-guided acceleration strategy for multi-scale weighted nuclear norm minimization-based image denoising

By exploring the nonlocal self-similarity of images, weighted nuclear norm minimization (WNNM) based image denoising algorithm has achieved competitive performance. Multi-scale patches-based image denoising using weighted nuclear norm minimization (MS-WNNM) is one of the follow-up researches, which further improves WNNM by considering cross-scale self-similarity in block-matching. However, the better performance is achieved at the price of spending more time in a larger search space. In this paper, we propose an edge map-guided strategy to accelerate MS-WNNM based image denoising. Inspired by the fact that patches belonging to the structure part and patches belonging to the texture part follow different repetitive patterns, we propose a prior that the patches containing and surrounding the edges carry most of the structural and textural information of the image. The proposed prior is explained from an information-theoretic perspective. Based on this prior, an edge map-guided acceleration strategy is designed, where redundant patches are trimmed off and the informative patches are retained. Block-matching in the pruned search space, which still has patch diversity, can significantly reduce time without much loss of quality. As a result, comparable denoising can be achieved in less runtime. The experimental results demonstrate the quality and efficiency of the proposed method.

Robust sparse time‐frequency analysis for data missing scenarios

Sparse time-frequency analysis (STFA) can precisely achieve the spectrum of the local truncated signal. However, when the signal is disturbed by unexpected data loss, STFA cannot distinguish effective signals from missing data interferences. To address this issue and establish a robust STFA model for time-frequency analysis (TFA) in data loss scenarios, a stationary Framelet transform-based morphological component analysis is introduced in the STFA. In the proposed model, the processed signal is regarded as a sum of the cartoon, texture and data-missing parts. The cartoon and texture parts are reconstructed independently by taking advantage of the stationary Framelet transform. Then, the signal is reconstructed for STFA. The forward-backwards splitting method is employed to split the robust STFA model into the data recovery and robust time-frequency imaging stages. The two stages are then solved separately by using the alternating direction method of multipliers (ADMM). Finally, several experiments are conducted to show the performance of the proposed robust STFA method under different data loss levels, and it is compared with some existing state-of-the-art time-frequency methods. The results indicate that the proposed method outperforms the compared methods in obtaining the sparse spectrum of the effective signal when data are missing. The proposed method has a potential value in TFA in scenarios where data is easily lost.

Self-Supervised Depth Completion Based on Multi-Modal Spatio-Temporal Consistency

Due to the low cost and easy deployment, self-supervised depth completion has been widely studied in recent years. In this work, a self-supervised depth completion method is designed based on multi-modal spatio-temporal consistency (MSC). The self-supervised depth completion nowadays faces other problems: moving objects, occluded/dark light/low texture parts, long-distance completion, and cross-modal fusion. In the face of these problems, the most critical novelty of this work lies in that the self-supervised mechanism is designed to train the depth completion network by MSC constraint. It not only makes better use of depth-temporal data, but also plays the advantage of photometric-temporal constraint. With the self-supervised mechanism of MSC constraint, the overall system outperforms many other self-supervised networks, even exceeding partially supervised networks.

A Novel Adaptive Fractional Differential Active Contour Image Segmentation Method

When the image is affected by strong noise and uneven intensity, the traditional active contour models often cannot obtain accurate results. In this paper, a novel adaptive fractional differential active contour image segmentation method is proposed to solve the above problem. At first, in order to extract more texture parts of the image, an adaptively fractional order matrix is constructed according to the gradient information of the image, varying the fractional order of each pixel. Then, the traditional edge-stopping function in the regularization term is susceptible to noise, and a new fractional-order edge-stopping function is designed to improve noise resistance. In this paper, a fitting term based on adaptive fractional differentiation is introduced to solve the problem of improper selection of the initial contour position leading to inaccurate segmentation results so that the initial contour position can be selected arbitrarily. Finally, the experimental results show that the proposed method can effectively improve the segmentation accuracy of noise images and weak-edge images and can arbitrarily select the position selection of the initial contour.

AN INFRARED AND VISIBLE IMAGE FUSION METHOD BASED ON FUZZY PULSE COUPLED NEURAL NETWORK AND SALIENCE DETECTION

Existing image fusion methods based on pulse coupled neural network (PCNN) ignore the fact that the human brain is essential to the organic combination of neural network and fuzzy system. As a result, some pixels that carry detailed information cannot activate neurons, thereby resulting in loss of details of source images and lowering the quality of fused images. To solve this problem, the present study proposed an infrared and visible image fusion method based on fuzzy pulse coupled neural network (FPCNN) and salience detection. This proposed method constructed FPCNN by combining the fuzzy theory and pulse coupled neural network. The cartoon parts obtained from the decomposition of source images by the total variation model were fused based on FPCNN. Then, salience detection of coefficients of the convolution sparse representation of the texture parts was conducted. The texture parts were fused on the basis of salience detection results. Finally, the fused cartoon and texture parts were added to obtain the fused image. The effectiveness and superiority of the proposed method were verified by experiments. Results demonstrate that compared with the most advanced seven algorithms, the proposed algorithm is superior in terms of objective evaluation indexes such as information entropy, average gradient, structural similarity, and fusion performance based on gradient. Moreover, the proposed method has good visual effect. This study provides a reference for producing high-quality all-weather images in whole-scene description. Keywords: Image fusion, Pulse coupled neural network, Salience detection, Convolution sparse representation

A Coupled Variational System for Image Decomposition along with Edges Detection

In order to better decompose the images and protect their edges, in this paper, we proposed a coupled variational system consisting of two steps. The first step, an improved weighted variational model is introduced to obtain the cartoon and texture. Using the obtained cartoon image, in the second step, a new vector function is obtained for describing the pseudo edge of the considered image by one Tikhonov regularization variational model. Because Tikhonov regularization model is equivalent to carrying out a Gaussian linear filtering, the obtained vector function is smoother. To solve the coupled system, we give the alternating direction method, primal-dual method and Gauss-Seidel iteration. Using the coupled system, we can not only separate out the cartoon and texture parts, but also extract the edge. Extensive numerical experiments are given to show the effectiveness of the proposed method compared with other variational methods.

HFF-SRGAN: super-resolution generative adversarial network based on high-frequency feature fusion

At present, generative adversarial network (GAN) has made outstanding progress for image super-resolution (SR), solving the problem of edge smoothing in SR images. Nevertheless, there are also problems with a lack of texture details, noise, and artifacts. The high-frequency (HF) information of an image is significant for keeping the texture part of the reconstructed image, and the introduction of HF information can increase the learning ability of super-resolution generative adversarial network (SRGAN) for the image features, so we propose a super-resolution GAN based on high-frequency feature fusion (HFF-SRGAN), which can increase the texture detail of the reconstructed image and reduce the noise and artifacts with less computational cost. Experimental results show that our algorithm evaluates significantly better than SRGAN and has significant advantages in texture details and noise compared with other SR algorithms.

An Artistic Image Fusion Method with Improved Cartoon-Texture Decomposition

When the art images are restored by the virtual restoration method, there are problems such as insufficient clarity and more noise in the reference image. An improved cartoon-texture decomposition method for art image fusion is proposed. The nonlinear local total variation component is used as the indicator function of image decomposition to obtain the image cartoon structure component and texture oscillation component. According to the oscillation component’s strong repetitiveness and structural directionality, the image texture part is filtered by combining the improved directional diffusion algorithm. Using the sparse coefficients of the fused cartoon component and the sparse coefficients of the texture component, the cartoon and texture of the image is inverse transformed and weighted and summed to obtain the recovered image after fusion. The experimental results show that this paper has a good effect after image fusion, and the recovered clarity is higher, which can better express the basic information of the source image; compared with several decomposition fusion methods commonly used at present, this paper has better recovery performance and detail processing ability and preserves the edge information of essential details in the image while filtering and denoising and is more excellent in objective performance evaluation indexes such as PSNR and SSIM. It can be used as a reference basis in the restoration process of art images.

A fractional variational image denoising model with two-component regularization terms

Image denoising is to recover true image from noisy image. Many image deonising models are proposed during the last decades. Some models preserve the margin of tissue, i.e., TV model, while the others, i.e., LLT model, prefer smooth solutions. By decomposing true image into cartoon part and texture part, we propose a fractional image denoising model with two-component regularization terms. Setting some appropriate parameters, the proposed model can deal with both smooth and non-smooth image denosing problems. The existence and uniqueness of solution for the variational model are proved. Moreover, a Split-Bregman(S-B) based numerical algorithm to solve this model is also proposed to validate the theoretical results. Numerical tests show that the proposed model can produce competitive denoising result to the other three published models.

An Edge Preserving Image Denoising Framework Based on Statistical Edge Detection and Bilateral Filter

The task of reducing noise from an image is known as image denoising. Although there are various methods and algorithms proposed in the literature, the methods still have limitations. The approaches generally either fail to reduce noise adequately or cause to be lost while effectively reducing noise. Conventional methods have poor performance when considering the success of preserving region boundaries and small structures. Conversely, modern techniques are more effective to smooth images without over smoothing edge details. To address these deficiencies and benefits, in this paper, we aim to develop a framework, which is capable of detecting whether a pixel is a part of edges or textures in an image so the framework can decide which filter should be used depending on region information. The Rank Order Test Method is used to detect image edges. In this way, we determine both which neighbors should be included to build a filter mask in the calculation for each pixel and which filter method should be implemented. We have compared the performance of Bilateral Filter-based methods. Experiments demonstrate that the proposed framework outperforms in terms of both PSNR, SSIM and visual perception for the noise with standard deviations 10, 30, 50. While the average PSNR value was 30.33 DB for the proposed model, the method with the closest result achieved an average score of 28.33 DB.

Relative rhythms, urban oases, and spatial resilience / Exploring syntaxes of seclusion, solitude, and tranquility

This article engages with the role of what one might tentatively call “secondary” urban spaces, in that while they are public, they are not the most vibrant, populated, or active places. These are not the spaces envisioned in many project illustrations. They are not full of people and activity. They are however a crucial part of a wider texture of urban situations, and important to extending our understanding of seclusion, solitude, and tranquility beyond distant parks and recreation areas. My aim here is to understand the emergence of these spaces in-between; those that are close to the vibrant streets and are embedded in city centers yet which offer a respite from the most bustling urbanity. These spaces, I will argue, more easily allow for the kinds of interactions that can lead to bridging and bonding with the unknown, in addition to the important everyday encounters that occur on central streets and squares. Using qualitative methods which build on Lefebvre’s rhythmanalysis, the discussion will draw on observations of the syntactic properties that condition, enable, and characterize such spaces, and address a series of concepts, including capacity, insulation, sequencing, and interface. A better understanding of such places, it is argued, not only allows a richer set of tools for working with urban design and planning, but offers possibilities for more resilient planning in terms of generating social relations, the emergence of communities, and for cities to manage and withstand extraordinary conditions.

Micro‐ and Nanotexturization of Liquid Silicone Rubber Surfaces by Injection Molding Using Hybrid Polymer Inlays

AbstractMicro‐ and nanotexturization of surfaces can give to the parts different advanced functionalities, such as superhydrophobicity, self‐cleaning, or antibacterial capabilities. These advanced properties in combination with the biocompatibility of Liquid Silicone Rubber are an interesting approach for obtaining high‐performance medical devices. The industrial production of surface textures in polymeric materials is through the replication technique, and the best option to attain a high production rate is injection molding. Moreover, its low viscosity during processing can provide an accurate replication capacity by the easy filling by capillarity of the microtextures. An innovative replicating technique for Liquid Silicone Rubber is presented by studying the replication of different shaped textures within a diameter range of between 2 and 50 µm. The copying process consists in the overmolding of a textured polymeric inlay obtained by nanoimprint lithography. At the end of the process, a textured part is obtained, while the imprinted film remains in the mold. The injection molding parameters are optimized to increase the replication accuracy, and their effect on texture replicability is analyzed and discussed. Finally, it is shown that the textured surfaces improve their wettability behavior, which is a necessary and important characteristic in the development of biomedical devices.

Heritage, Culture and Regeneration of the Former Military Areas in the City of Oradea, Romania

There are places where history is still alive: heritage sites, locations of great cultural, scientific, educational, and social significance. The military presence in the city of Oradea, Romania, generated an impressive cultural-historical heritage, both tangible and intangible, as the consequence of centuries of alternative militarization and demilitarization processes. The aim of the article is to explain that the former military areas are great sustainable historical, cultural, architectural and socio-economic local assets, with a specific function in the urban texture. The research methodology combines a review of the relevant literature, fieldwork, analysis of the evolution and status of the former military areas, and synthesis to help reach our conclusions. The results of the study underline the great significance and role of the former military sites in the urban structure, society and economy, by providing important functions like tourism, education, public administration, military, residential, commerce or industry. The main issue is the transformation of the abandoned military heritage sites into potential elements of sustainable urban development to create a new and vivid part of urban texture. Built heritage of former military areas is found across many countries and this article explain the particular common features, helping sharing experiences, know-how, best practices on the transformation of military sites to public uses in Europe and around the world.

Image decomposition based on nonlinear reaction–diffusion systems

The separation of image content into semantic parts plays a vital role in applications such as compression, enhancement, restoration, and more. In recent years, several pioneering works suggested such a separation be based on variational formulation and others using independent component analysis and sparsity. This paper presents a novel method for separating images into texture and piecewise smooth (cartoon) parts. The new model is based on a class of degenerate and singular reaction–diffusion systems coupled by the minimizing total variation flow and the p-Laplace flow. The existence and uniqueness of entropy solutions to the system with BV initial data and Neumann boundary conditions are established by the regularization method. Experimental results illustrate that the new model can preserve textures better than other methods.