Texture Structure Research Articles

Style Migration Based on the Loss Function of Location Information

Using the improved Johnson et al.’s style migration network as a starting point, this paper proposes a new loss function based on the position information Gram matrix. The new method adds the chunked Gram matrix with position information, and simultaneously, the structural similarity between the style map and the resultant image is added to the style training. The style position information is given to the resultant image, and finally, the resolution of the resultant image is improved with the SRGAN. The new model can effectively migrate the texture structure as well as the color space of the style image, while the data of the content image are kept intact. The simulation results reveal that the image processing results of the new model improve those of the classical Johnson et al.’s method, Google Brain team method, and CCPL method, and the SSIM values of the resulting map and style image are all greater than 0.3. As a comparison, the SSIM values of Johnson et al., Google Brain team, and CCPL are 0.14, 0.11, and 0.12, respectively, which is an obvious improvement. Moreover, with deeper training, the new method can improve the similarity of certain resulting images and style images to more than 0.37256. In addition, training other arbitrary content images on the basis of the trained model can quickly yield satisfactory results.

Understanding changes of laser backscattering imaging parameters through the kiwifruit softening process using time series analysis

ABSTRACT During kiwifruit storage, quality monitoring is required for inventory planning and consistent quality maintenance. Commercial near-infrared (NIR) spectrophotometers showed reduced performance in the estimation of kiwifruit flesh firmness (FF) as the FF estimation is indirect and can be affected when both, textural structures and absorbing compounds, change during postharvest ripening. Laser backscattering imaging (LBI) records the backscattered signal after a single laser beam interacts with kiwifruit tissue, including merged information on light absorption and scattering. Measurements were carried out at 830 nm, where scattering is most dominant. In this work, time series of kiwifruit ‘Zesy002’ (n = 30) and ‘Hayward’ (n = 30) LBI were collected through the postharvest ripening during a 15-day shelf life at 20°C. Four LBI parameters capturing DIP, FWHM, SLP and HWQM were selected in this study. ‘‘Zesy002’ DIP, FWHM, SLP, and HWQM increased approx. 0.6 cm, 0.2 cm, 0.3 and 0.14 cm, respectively. ‘Hayward’ LBI increased approx. 0.2 cm, 0.1 cm, 0.2 and 0.04 cm, respectively. Different initial LBI values between cultivars and LBI changes may reflect the actual stage of softening, affected by kiwifruit ripeness. In conclusion, time series analysis could be useful in describing kiwifruit LBI change during ripening and making forecasts.

Features of designing a Component Element of the National women's Azerbaijani Costume - Kelagai Scarfs

Presents studies of the parameters of the structure and design of Azerbaijani national scarves kelagai. A historical analysis of the development of kelagai production has been carried out, which reflects the ethnographic, historical, artistic and technological features of folk art. Proposals have been developed for the use of kelagai in the design of modern fashionable women's clothing. The features of making kelagai scarves have been studied and the factors influencing their structure and structure, as well as their role in the formation of national characteristics of modern fashion, have been identified. The structure and structure of kelagai scarves was studied based on the texture of the fabric structure. The conducted research makes it possible to use the national traditions of making kelagai scarves in the design of modern clothing, as one of the iconic and symbolic elements of folk culture and an important component of the artistic and compositional patterns of shaping promising models of modern women's clothing, as well as for the dynamic development of material and spiritual culture.

TD Swin-UNet: Texture-Driven Swin-UNet with Enhanced Boundary-Wise Perception for Retinal Vessel Segmentation.

Retinal vessel segmentation plays a crucial role in medical image analysis, aiding ophthalmologists in disease diagnosis, monitoring, and treatment guidance. However, due to the complex boundary structure and rich texture features in retinal blood vessel images, existing methods have challenges in the accurate segmentation of blood vessel boundaries. In this study, we propose the texture-driven Swin-UNet with enhanced boundary-wise perception. Firstly, we designed a Cross-level Texture Complementary Module (CTCM) to fuse feature maps at different scales during the encoding stage, thereby recovering detailed features lost in the downsampling process. Additionally, we introduced a Pixel-wise Texture Swin Block (PT Swin Block) to improve the model's ability to localize vessel boundary and contour information. Finally, we introduced an improved Hausdorff distance loss function to further enhance the accuracy of vessel boundary segmentation. The proposed method was evaluated on the DRIVE and CHASEDB1 datasets, and the experimental results demonstrate that our model obtained superior performance in terms of Accuracy (ACC), Sensitivity (SE), Specificity (SP), and F1 score (F1), and the accuracy of vessel boundary segmentation was significantly improved.

Air-mediated phosphoric acid activation strategy for preparation of oxygenated functional groups rich activated carbon for capacitive deionization

Atmosphere during H3PO4 activation is vital to the texture structure and surface-interface properties of activated carbon (AC), but the researches are way out of sufficiency. In this work, it is found that the air in the H3PO4 activation process is not only conducive to the introduction of more-oxidized oxygenated functional groups on ACs, but also beneficial to the formation of micropores rather than mesopores. Besides, as the air content increases in H3PO4 activation, the ACs’ ash, phosphorous content and conductivity reduce, but their wettability, disorder degree and defect number increase. In addition, the potential of zero charge of the as-prepared ACs is positively correlated with the O-CO content, which show a pattern of first increasing and then decreasing with the increase of air content. Benefit from the improved hydrophilicity, electrolyte-affinity, additional adsorption sites for sodium ions, the optimal AC exhibits excellent salt adsorption capacity of 12.08 mg g−1 and good cycling stability at 1.0 V in single-pass capacitive deionization. This work not only establishes a novel and effective method for regulating the texture structure and surface properties of ACs, but also provides promising cathode candidates for capacitive deionization application.

A low-light image enhancement model based on anisotropic weighted exponential local derivatives

Low-light images incur several complicated degradations such as low brightness, poor contrast, and higher noise. Retinex theory, which decomposes a low-light image into illumination and reflection components based on local image derivatives, is widely used in the field of shimmering image enhancement. In this paper, we propose a new low-light image enhancement model based on the Retinex theory to reduce the above distortions. To be more specific, we first introduce a new structural texture extractor (NSTE) based on the local image derivatives properties to generate the texture and structure maps of the observed images. Compared to existing structural texture extractors, NSTE has two key designs: anisotropic weighting and adaptive exponential term. The aim is to generate structural maps with clear edge and texture maps with salient details. Subsequently, these two images are used to regularize the illumination and reflection components of the variational decomposition model, respectively. Finally, we developed a luminance correction function and a detail enhancement function to enhance the illumination and reflectance, respectively. This aims to improve the viewing experience, mitigate over-enhancement, and minimize detail distortion. We choose the alternating direction method of multipliers (ADMM) to solve the optimization model. Experimental results on several low-light datasets demonstrate that our proposed method can achieve better quantitative and qualitative performance than state-of-the-art methods.

3D model topology algorithm based on virtual reality visual features

This study proposes a virtual reconstruction method for fuzzy 3D images based on visual communication effects to improve recognition ability. This method includes constructing a grid distribution model for 3D images, recombining spatial information features using visual feature extraction, and detecting edges using contour feature extraction. A three-dimensional image visual communication model was established, and adaptive pixel reconstruction was achieved through fuzzy structure reconstruction method. Reconstruct the texture structure and grayscale histogram of 3D images to achieve high-quality virtual reconstruction. The simulation results verified the effectiveness of the method, and the designed simulation platform verified the accuracy and completeness of the method, demonstrating the improvement of existing systems and the potential for advancing the field of visual communication.

Deep learning-based spinal canal segmentation of computed tomography image for disease diagnosis: A proposed system for spinal stenosis diagnosis.

Lumbar disc herniation was regarded as an age-related degenerative disease. Nevertheless, emerging reports highlight a discernible shift, illustrating the prevalence of these conditions among younger individuals. This study introduces a novel deep learning methodology tailored for spinal canal segmentation and disease diagnosis, emphasizing image processing techniques that delve into essential image attributes such as gray levels, texture, and statistical structures to refine segmentation accuracy. Analysis reveals a progressive increase in the size of vertebrae and intervertebral discs from the cervical to lumbar regions. Vertebrae, bearing weight and safeguarding the spinal cord and nerves, are interconnected by intervertebral discs, resilient structures that counteract spinal pressure. Experimental findings demonstrate a lack of pronounced anteroposterior bending during flexion and extension, maintaining displacement and rotation angles consistently approximating zero. This consistency maintains uniform anterior and posterior vertebrae heights, coupled with parallel intervertebral disc heights, aligning with theoretical expectations. Accuracy assessment employs 2 methods: IoU and Dice, and the average accuracy of IoU is 88% and that of Dice is 96.4%. The proposed deep learning-based system showcases promising results in spinal canal segmentation, laying a foundation for precise stenosis diagnosis in computed tomography images. This contributes significantly to advancements in spinal pathology understanding and treatment.

DF3Net: Dual frequency feature fusion network with hierarchical transformer for image inpainting

In recent years, researchers have made significant strides in computer vision by leveraging transformers, achieving remarkable breakthroughs in low-level vision tasks. The inherent long-range dependency of transformers grants them potent remote modeling capabilities, surpassing those of Convolutional Neural Networks (CNNs) and enabling the extraction of global features and accurate semantic structures. However, it has been observed that single transformer frameworks lack sensitivity to high-frequency information in images, resulting in the generation of blurry reconstructed regions. To address this limitation, this paper proposes a novel two-branch Dual Frequency Feature Fusion Network (DF3Net) for image inpainting based on the hierarchical atrous transformer (HAT). Specifically, the head of the dual-frequency convolution (DFC) module decouples the feature maps into low and high-frequency components. The low-frequency factor goes through the proposed HAT branch, while the high-frequency component is input into the gated convolution module branch, effectively capturing both global structural information and local texture details. Finally, the DFC tail fuses the high and low-frequency features to output the reconstructed image. Moreover, the feature fusion between high and low-frequency branches is performed layer-wise within the network, enabling mutual learning between the two branches interactively, ensuring coherence in image semantic structure and texture details. Experimental evaluations on Places2, Paris StreetView, and CelebA-HQ with different mask ratios demonstrate that the proposed method outperforms state-of-the-art methods in enhancing the structural accuracy of image inpainting. It generates semantically reasonable images with fine texture details.

Geo-mechanical and geo-morphology characterisation of the cap rocks in the Niger Delta for potential carbon capture and storage

The world has been battling climate change which is caused by an excessive amount of carbon dioxide in the atmosphere. Carbon sequestration in geological sinks was identified as one of the major methods to curb and reduce the amount of carbon dioxide in the atmosphere. The carbon dioxide stored in the geological formations must not escape back into the atmosphere. Characterisation of potential reservoirs for geological sequestration is pertinent for injectivity, capacity, and most importantly containment of carbon dioxide. The cap rock is one of the most important factors determining carbon sequestration success. This study focuses on the preliminary characterisation of the caprocks for evaluating potential subsurface storage of carbon dioxide based on their petrophysical characteristics. X-ray Diffraction and X-ray Fluorescence provided the mineralogical composition and geochemical data of the cap rocks while Scanning Electron Microscopy-EDS was used to identify the qualitative information on the micromorphological textural structure of the cap rocks in the Niger Delta. Conventional Triaxial experiments were used to determine the elastic and geo-mechanical strength of the caprocks. It was identified that the grain skeleton of caprocks in the region is predominated by Quartz, Albite, and Muscovite. The ratio of the tectosilicate and phyllosilicate minerals indicates that the cap rocks are silicate shale. The peak strength of the caprocks ranged from 48.85 to 80.50 MPa and are classified as strong rocks. The cap rocks showed a quasi-elastic behaviour after being subjected to compressive axial force. The elastic modulus of the caprocks was also observed. The characteristics exhibited by the shale caprock at the rock matrix level are highly favourable for sealing carbon dioxide and hence indicate capability for use in carbon sequestration.

Modular flavor symmetric models

We review the modular flavor symmetric models of quarks and leptons focusing on our works. We present some flavor models of quarks and leptons by using finite modular groups and discuss the phenomenological implications. The modular flavor symmetry gives interesting phenomena at the fixed point of modulus. As a representative, we show the successful texture structure at the fixed point [Formula: see text]. We also study CP violation, which occurs through the modulus stabilization. Finally, we study SMEFT with modular flavor symmetry by including higher dimensional operators.

Petrogenesis and Tectonic Setting of the Baluogenguole Mafic Dykes, Zongwulong Belt: Implications for Evolution of the Northern East Paleo-Tethys Ocean

The late Paleozoic tectonic setting of the Zongwulong Belt (ZWLB), a significant unit located in the northern Qaidam margin, Qinghai province, remains uncertain. Diabase dykes in the western part of the Zongwulong Belt offer insights into this issue. Field investigations reveal that the dips of the dykes are almost vertical, and they have sharp boundaries with the host rocks. These dykes consist of plagioclase, clinopyroxene, and opaque minerals exhibiting a characteristic porphyritic texture and massive structure. Zircon U-Pb dating of the dykes yields a weighted 206Pb/238U age of 289 ± 1 Ma. The dykes exhibit relatively high concentrations of TFeO, K2O + Na2O, and TiO2, while the SiO2 and MgO concentrations are relatively low. They display relative light-over-heavy REE enrichment, and lack negative Nb-Ta and Eu anomalies. The dykes underwent negligible crustal contamination, and experienced extensive fractional crystallization of olivine, clinopyroxene, and Fe-Ti oxides. Originating from the spinel–garnet transition zone at depths of approximately 75 km, the dykes result from garnet facies low-degree melting (5%–10%) in a continental rift setting. Combining these findings with regional geological data, we propose that the ZWLB likely experienced a continental rift in the west and exhibited a narrow oceanic environment in the east in the late Paleozoic period, potentially representing the most distant north branch of the East Paleo-Tethys Ocean.

Hierarchical Structure with Microcrater Covered with Nanograss Enhancing Condensation and Its Antifrosting/Anti-Icing Performance Inspired by Euphorbia helioscopia L.

Over an extended period of evolution and natural selection, a multitude of species developed a diverse array of biological interface features with specific functions. These biological structures provide a rich source of inspiration for the design of bionic structures on superhydrophobic surfaces. Understanding the functional mechanism of plant leaves is of paramount importance for the advancement of new engineering materials and the further promotion of engineering applications of bionic research. The hierarchical structure of microcrater-covered nanograss (MCNG) on the surface of E. helioscopia L. leaf provided the inspiration for the bionic MCNG surface, which was successfully prepared on a copper substrate by hybrid laser micromachining technology and chemical etching. The combined action of texture structure and surface chemistry resulted in a contact angle of 169° ± 1° for MCNG surface droplets and a rolling angle of less than 1°. Notably, the condensation-induced adhesion force does not augment with the increase of the temperature difference, which facilitated the shedding of hot droplets from the surface. The microscope observation revealed a high density of condensed droplets on the MCNG surface and the tangible jumping behavior of the droplets. The fabricated MCNG also demonstrated excellent antifrost/anti-icing abilities in low-temperature and high-humidity environments. Finally, the study confirmed the exceptional mechanical durability and reusability of the MCNG surface through various tests, including scratch damage, sandpaper wear, water flow impact and flushing, and condensation-drying cycle tests. The nanograss can be effectively protected within the microcrater structure. This research presents a promising approach for preventing and/or removing unwanted droplets in numerous engineering applications.

A blind image super-resolution network guided by kernel estimation and structural prior knowledge

The goal of blind image super-resolution (BISR) is to recover the corresponding high-resolution image from a given low-resolution image with unknown degradation. Prior related research has primarily focused effectively on utilizing the kernel as prior knowledge to recover the high-frequency components of image. However, they overlooked the function of structural prior information within the same image, which resulted in unsatisfactory recovery performance for textures with strong self-similarity. To address this issue, we propose a two stage blind super-resolution network that is based on kernel estimation strategy and is capable of integrating structural texture as prior knowledge. In the first stage, we utilize a dynamic kernel estimator to achieve degradation presentation embedding. Then, we propose a triple path attention groups consists of triple path attention blocks and a global feature fusion block to extract structural prior information to assist the recovery of details within images. The quantitative and qualitative results on standard benchmarks with various degradation settings, including Gaussian8 and DIV2KRK, validate that our proposed method outperforms the state-of-the-art methods in terms of fidelity and recovery of clear details. The relevant code is made available on this link as open source.

Rice husk waste-derived super-biochar with the max surface area and Philic-CO2 textural structure: Boosting effect and mechanism of post-desilication

A super-carbon material as adsorbents of CO2 were prepared with rice husk waste by a new two-step process, pyrolysis-activation and HF post-desilication process. This paper was focused on investigating the boosting effect and mechanism of post-desilication. It was found that the function of the post-desilication was considerably different from that of the reported pre-desilication. It could not only effectively remove Si element from rice husk biochar (RHC) but also significantly improve its performance, producing a super-biocarbon with a well-developed pore structure, rich C-F groups, and a 4230-m2 g−1 surface area, which is the max surface area of known biocarbon. It could adsorb 341.5 mg g−1 CO2, a max CO2 adsorption capacity of reported biocarbon, and its CO2/N2 and CO2/H2O adsorption selectivity index reached as high as 22.2 and 2.1, respectively. The large surface area was mainly originated from the expansion effect of SiF4 gas from the post-desilication, generating numerous ultra-microspores, while these C-F groups were generated by the co-reactions of the post-desilication. The ultra-high adsorption capacity was mainly from the fill-adsorption driven by the size effect of these ultra-micropores and the strong CO2-affinity of C-F groups, and the adsorption selectivity was confirmed to depend on water-resistant and CO2-philic characteristics of its C-F groups by theoretical calculations and experimental observations. These findings presented not only an advanced CO2-capture material but also a facile way to tailor textural structure of other biochars.

A novel insight of solid–liquid interaction effect during laser hybrid manufacturing Ti2AlNb alloy

Combining interlayer point-forging with laser-deposition (PF-LD) provides a new idea to in-situ modify coarse-grained structure and strong solidification texture during high-thermal additive manufacturing process. In this study, a well-designed step-experiment with one to three-pass PF-LD was conducted on columnar-grained Ti2AlNb substrate to elucidate a novel insight of solid–liquid interaction effect. The results revealed that the combination of localized micro-forging and laser re-heating was capable to produce randomly-distributed equiaxial structure via interlayer recrystallization behavior, at the same time it in turn had a positive effect on solidification grain refinement. The essential contribution of these fine-grained microstructures further created an acceleration effect to interlayer recrystallization behavior and promoted the full-equiaxial grain morphology of PF-LD Ti2AlNb part.

Influence of different indica rice (Oryza sativa L. subsp. indica Kato) flours on the quality and flavor characteristics of fermented rice cakes

AbstractBackgrounds and ObjectivesFermented rice cake is a common traditional fermented food in China with a crystal clear color, unique flavor, dense honeycomb structure, and soft and delicate texture. As its raw materials are not uniform, different batches often have poor product quality stability and different volatile flavor components. Therefore, this study investigates the effects of different indica rice flours on processing adaptability and their impacts on the flavor characteristics.FindingsThe results show that the seven indica rice flours have significantly different basic compositions and physicochemical properties (p < .05). Specifically, the fermented rice cake prepared with TG2 showed a large specific volume, good elasticity, bright color, high volatile flavor compound contents, and the maximum comprehensive sensory evaluation score. Additionally, partial least squares discriminant analysis was conducted, and three volatile compounds were identified with variable importance for the projection >1 and odor activity value >1 as the criteria, namely, ethanol, styrene, and phenethyl alcohol, which played major roles in forming the overall aroma of fermented rice cakes.ConclusionsThe texture, color, and sensory score of fermented rice cakes are closely related to the basic components of the indica rice flour, and the most popular fermented rice cakes have a specific volume of no less than 2 mL/g, an L* of above 78, and a TPA‐tested elasticity of about 3 mm.Significance and NoveltyThis study may help improve the processing of fermented rice cakes and provide a theoretical basis for their production and flavor control.

Research on Surface Defect Detection of Strip Steel Based on Improved YOLOv7.

Surface defect detection of strip steel is an important guarantee for improving the production quality of strip steel. However, due to the diverse types, scales, and texture structures of surface defects on strip steel, as well as the irregular distribution of defects, it is difficult to achieve rapid and accurate detection of strip steel surface defects with existing methods. This article proposes a real-time and high-precision surface defect detection algorithm for strip steel based on YOLOv7. Firstly, Partial Conv is used to replace the conventional convolution blocks of the backbone network to reduce the size of the network model and improve the speed of detection; Secondly, The CA attention mechanism module is added to the ELAN module to enhance the ability of the network to extract detect features and improve the effectiveness of detect detection in complex environments; Finally, The SPD convolution module is introduced at the output end to improve the detection performance of small targets with surface defects on steel. The experimental results on the NEU-DET dataset indicate that the mean average accuracy (mAP@IoU = 0.5) is 80.4%, which is 4.0% higher than the baseline network. The number of parameters is reduced by 8.9%, and the computational load is reduced by 21.9% (GFLOPs). The detection speed reaches 90.9 FPS, which can well meet the requirements of real-time detection.

Image Inpainting based on Dilated Neighborhood Attention

In response to the phenomenon that existing image restoration algorithms have blurry edges, incoherent textures, and lack clarity and delicacy for large-area missing images, a two-stage generative adversarial image restoration algorithm based on dilated neighborhood attention is proposed. This algorithm decouples image restoration into edge structure restoration and texture structure restoration, introduces a dilated neighborhood attention mechanism, and enhances the generator's focus on important information and structures in the image by constructing a residual attention network, thereby improving the perception and utilization of texture details, resulting in more realistic image views and finer texture details. This paper introduces the Binary Cross-Entropy with Logits loss function in the discriminators of the two stages, which can help the discriminator learn how to more effectively identify real and generated images, thus improving the overall network performance. The Ranger21 optimizer is introduced to accelerate learning without affecting generalization, addressing the problem of traditional optimizers systematically staying in poor initial states. The datasets used in this paper are Paris Street View and CelebA-HQ. Comparative experiments with other image restoration methods show that both peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) have improved, and the larger the mask area, the more significant the improvement. Experiments prove that the images restored by the proposed algorithm have more reasonable structures and richer details, and the image restoration effect is superior.

Study on the Wettability and Abrasion Resistance of Ultrafast-Laser-Textured Ti Surface

Titanium (Ti) materials are highly valued in the medical field for their outstanding biocompatibility and corrosion resistance. However, challenges such as suboptimal wettability and wear resistance can impact the tribological properties of titanium implants, potentially leading to implant failure. This study explores the application of ultrafast pulsed laser processing to create two distinct structures, circular pits and grooves, on the surface of titanium materials. The samples underwent low-surface-energy treatment, after which the wettability and wear resistance of the textured surfaces were evaluated. The findings indicate that the textured surfaces exhibit improved hydrophobic properties and reduced surface wear. Specifically, the textured surfaces demonstrated a remarkable 73.68% reduction in wear compared to the untextured surfaces. These results underscore the potential of etching textured structures onto titanium surfaces to enhance their wear resistance, thereby offering promising implications for the improvement of titanium implant performance.