Texture Enhancement Research Articles

Enhanced Rheological and Structural Properties of the Exopolysaccharide from Rhizobium leguminosarum VF39 Through NTG Mutagenesis

Microbial exopolysaccharides (EPSs) are biopolymer materials with advantages such as biodegradability, biocompatibility, ease of mass production, and reproducibility. The EPS that was isolated from Rhizobium leguminosarum bv. viciae VF39 is an anionic polysaccharide with a backbone structure consisting of one galactose, five glucose molecules, and two glucuronic acids, along with 3-hydroxybutanoyl, acetyl, and pyruvyl functional groups. Through N-methyl-N′-nitro-N-nitrosoguanidine (NTG) mutagenesis, we isolated and purified a mutant EPS from VF39, VF39 #54, which demonstrated enhanced physicochemical and rheological properties compared to the wild-type VF39. The EPS structure of the VF39 #54 mutant strain showed a loss of glucuronic acid and 3-hydroxybutanoyl groups compared to the wild-type, as confirmed by FT-IR, NMR analysis, and uronic acid assays. The molecular weight of the VF39 #54 EPS was 250% higher than that of the wild-type. It also exhibited improved viscoelasticity and thermal stability. In the DSC and TGA analyses, VF39 #54 had a higher endothermic peak (172 °C) compared to the wild-type (142 °C), and its thermal decomposition point was 260 °C, surpassing the wild-type’s value of 222 °C. Additionally, the VF39 #54 EPS maintained a similar viscosity to the wild-type in various pH, temperature, and metal salt conditions, while also exhibiting a higher overall viscosity. The cytotoxicity test using HEK-293 cells confirmed that the VF39 #54 EPS was non-toxic. Due to its high viscoelastic properties, the VF39 #54 EPS shows potential for use in products such as thickeners, texture enhancers, and stabilizers. Furthermore, its thermal stability and biocompatibility make it a promising candidate for applications in food, pharmaceuticals, and cosmetic formulations. Additionally, its ability to maintain viscosity under varying environmental conditions highlights its suitability for industrial processes that require consistent performance.

Image-enhanced endoscopy in upper gastrointestinal disease: focusing on texture and color enhancement imaging and red dichromatic imaging

Image-enhanced endoscopy in upper gastrointestinal disease: focusing on texture and color enhancement imaging and red dichromatic imaging

Effects of fish meal replacement with composite proteins source on growth performance, antioxidant capacity and swim bladder quality of chu’s croaker (Nibea coibor)

Collagen, as a commercial functional protein, is widely applied in food, cosmetics, biomedicine and other industries, and swim bladder is regarded as an ideal source of raw materials due to its rich collagen content. Chu’s croaker (Nibea coibor), as an economical fish that can produce high-grade swim bladder, is popularly farmed, however, its high fishmeal demand limits the development of the feed industry. Therefore, it is worth thinking about how to reduce the use of fish meal without affecting the swim bladder quality. Given that, an 8-week feeding trial was conducted to investigate the effects of fish meal partially replaced (0.0%, 5.0%, 10.0%, 15.0%, 20.0%) with composite proteins (CPs, consisted of corn protein meal and hydrolyzed feather meal) on growth, antioxidant capacity and swim bladder quality (texture and collagen deposition) of chu’s croaker. Results showed that although growth performance decrease with the increase of CPs’ replacement level, it did not compromise the swim bladder quality. Specifically, 5% - 10% CPs did not affect the growth significantly, but can improve antioxidant capacity, protein content, collagen synthesis and texture quality of swim bladder. The enhancement of swim bladder texture, including hardness, chewiness, springiness, and adhesiveness may be caused by changes in the expression of related genes, such as colga1t1, col12a1, cyp27a1, etc. These results indicated that appropriate fish meal replaced by CPs could be applied during aquaculture practice after comprehensive consideration of feed cost, growth rate and quality of swim bladder. This study provides support for the development of cost-effective feed formulations that improve the sustainability and economic viability of chu’s croaker culture.

The role of physical chemistry in food and beverage

Physical chemistry is a fundamental science that underpins many critical processes in food and beverage production. This paper explores the influence of physico-chemical properties on basic reactions such as crystallization, enzyme kinetics in fermentation, and the Maillard reaction, all of which play a significant role in determining the flavor, texture, stability, and quality of food products. By examining the effects of factors like temperature, pH, solute concentration, and the presence of additives, we demonstrate physical chemistry principles guide the manipulation of these processes to optimize product characteristics. Understanding these interactions allows for greater control over food production techniques, leading to innovations in texture enhancement, flavor development, and preservation. This study highlights the importance of physical chemistry in advancing the food and beverage industry by providing insights into the molecular mechanisms that influence the sensory and functional properties of products.

Joint Optimization-Based Texture and Geometry Enhancement Method for Single-Image-Based 3D Content Creation

Recent studies have explored the generation of three-dimensional (3D) meshes from single images. A key challenge in this area is the difficulty of improving both the generalization and detail simultaneously in 3D mesh generation. To address this issue, existing methods utilize fixed-resolution mesh features to train networks for generalization. This approach is capable of generating the overall 3D shape without limitations on object categories. However, the generated shape often exhibits a blurred surface and suffers from suboptimal texture resolution due to the fixed-resolution mesh features. In this study, we propose a joint optimization method that enhances geometry and texture by integrating generalized 3D mesh generation with adjustable mesh resolution. Specifically, we apply an inverse-rendering-based remeshing technique that enables the estimation of complex-shaped mesh estimations without relying on fixed-resolution structures. After remeshing, we enhance the texture to improve the detailed quality of the remeshed mesh via a texture enhancement diffusion model. By separating the tasks of generalization, detailed geometry estimation, and texture enhancement and adapting different target features for each specific network, the proposed joint optimization method effectively addresses the characteristics of individual objects, resulting in increased surface detail and the generation of high-quality textures. Experimental results on the Google Scanned Objects and ShapeNet datasets demonstrate that the proposed method significantly improves the accuracy of 3D geometry and texture estimation, as evaluated by the PSNR, SSIM, LPIPS, and CD metrics.

Fava Bean Protein Nanofibrils Modulate Cell Membrane Interfaces for Biomolecular Interactions as Unveiled by Atomic Force Microscopy.

Functional amyloids (protein nanofibrils, PNF) synthesized from plant sources exhibit unique physicochemical and nanomechanical properties that could improve food texture. While environmental factors affecting PNFs are well-known, scientific evidence on how cells (focus on the oral cavity) respond to them under physiological conditions is lacking. Self-assembled PNFs synthesized from fava bean whole protein isolate show a strong pH- and solvent-dependent morphology and elasticity modification measured by atomic force microscopy (AFM). After incubation of PNFs with an oral mechanosensitive model cell line at pH 7.3, difference in cell-surface roughness without significant changes in the overall cell elasticity were measured. The role of cell membrane composition on supported lipid bilayers was also tested, showing an increase in membrane elasticity with increasing fibril concentration and the possible impact of annular phospholipids in binding. Genetic responses of membrane proteins involved in texture and fat perception were detected at the mRNA level by RT-qPCR assay and both mechano- and chemosensing proteins displayed responses highlighting an interface dependent interaction. The outcomes of this study provide a basis for understanding the changing physicochemical properties of PNFs and their effect on flavor perception by altering mouthfeel and fat properties. This knowledge is important in the development of plant-based texture enhancers for sensory-appealing foods that require consumer acceptance and further promote healthy diets.

Low-light image enhancement guided by multi-domain features for detail and texture enhancement

Low-light image enhancement holds significant value in the fields of computer vision and image processing, such as in applications like surveillance photography and medical imaging. Images captured in low-light environments typically suffer from significant noise levels, low contrast, and color distortion. Although existing low-light image enhancement techniques can improve image brightness and contrast to some extent, they often introduce noise or result in over-enhancement, leading to the loss of detail and texture. This paper introduces an innovative approach to low-light image enhancement by fusing spatial and frequency domain features while optimizing them with multiple loss functions. The core of the algorithm lies in its multi-branch feature extraction, multi-loss function constraints, and a carefully designed model structure. In particular, the model employs an encoder-decoder architecture, where the encoder extracts spatial features from the image, the Fourier feature extraction module captures frequency domain information, and the histogram feature encoder-decoder module processes global brightness distribution. These extracted features are then fused and reconstructed in the decoder to produce the enhanced image. In terms of loss functions, the algorithm combines perceptual loss, structural similarity loss, Fourier loss, and histogram loss to ensure comprehensive and natural enhancement effects. The novelty of this algorithm lies not only in its multi-branch feature extraction design but also in its unique model structure, which synergistically improves image quality across different domains, effectively preventing over-enhancement, and ultimately achieving a balanced enhancement of brightness, details, and texture. Experimental results on multiple datasets, including SIDD, LOL, MIT-Adobe-FiveK, and LOL-v2-synthetic, demonstrate that the proposed method outperforms existing techniques in terms of image detail, texture, and brightness. Specifically, it achieves a PSNR of 27.52 dB on the LOL dataset, surpassing Wavelet Diffusion by 1.19 dB. Additionally, on the LOL-v2-synthetic dataset, it achieves a PSNR of 29.56 dB, exceeding Wavelet Diffusion by 3.06 dB. These results demonstrate a significant enhancement in the visual quality of low-light images.

Textural enhancement and glycemic potency reduction of sugarcane fiber-incorporated white bread with ascorbic acid and xanthan gum

In this study, ascorbic acid (0.02 % w/w), xanthan (0.75 % or 1.5 % w/w), and their combination have been added into sugarcane fiber (SCF) incorporated (5 % or 10 % w/w) wheat flour-based white breads. The effects of different additives on the physical characteristics, and the in-vitro and in-vivo glycemic potency of breads were evaluated. Addition of xanthan alone and the combination of additives reduced hardness and increased specific volume. SEM images showed that xanthan caused larger, more uneven holes in breadcrumbs due to xanthan's high elasticity and viscosity. FTIR spectrum indicated that the combination of SCF, xanthan, and ascorbic acid resulted in higher β-turn, lower α-helix protein structures, and lower ratios of α-helix/β-sheet, indicating a more flexible gluten structure formed. In-vitro digestibility results suggested that all SCF-incorporated breads had a lower glycemic index (GI) value than reference. Samples with 0.02 % (w/w) ascorbic acid and 1.5 % (w/w) xanthan reported the lowest in-vitro and in-vivo GI values.

S914 Assessing the Effectiveness of Texture and Color Enhancement Imaging (TXI) versus White-Light Endoscopy (WLE) in Detecting Gastrointestinal Lesions: A Comprehensive Meta-Analysis

S914 Assessing the Effectiveness of Texture and Color Enhancement Imaging (TXI) versus White-Light Endoscopy (WLE) in Detecting Gastrointestinal Lesions: A Comprehensive Meta-Analysis

S2321 Visibility evaluation of Gastric Epithelial Neoplasm of Fundic Gland Mucosa Lineage in a New Image Enhanced Endoscopy; Texture and Color Enhancement Imaging

S2321 Visibility evaluation of Gastric Epithelial Neoplasm of Fundic Gland Mucosa Lineage in a New Image Enhanced Endoscopy; Texture and Color Enhancement Imaging

Comprehensive Evaluation of Pulmonary Masses Using Multi-detector CT: Correlating Morphological Features With CT-Guided Histopathological Findings for Enhanced Diagnostic Accuracy.

Lung masses pose a significant diagnostic challenge due to their diverse causes, from benign hamartomas to malignant bronchogenic carcinoma. Multi-detector CT (MDCT) is essential in evaluating these masses, offering detailed morphological insights to help differentiate between benign and malignant lesions. However, a definitive diagnosis often requires histopathological confirmation. CT-guided biopsy is crucial, providing a minimally invasive method to obtain tissue samples and thus guiding clinical management and treatment decisions. The primary objective of this study was to assess the diagnostic accuracy of MDCT in differentiating between benign and malignant lung mass lesions. The study focused on evaluating the characteristic features of lung masses on MDCT that aid in this differentiation, correlating imaging findings with histopathological results from CT-guided biopsies, and determining the overall diagnostic utility of MDCT in thoracic lesions. This hospital-based observational study was conducted over a period of 17 months. The study included 52 patients with thoracic lesions, identified through imaging techniques such as chest radiographs and CT scans. These patients underwent CT-guided biopsy, with tissue samples sent for histopathological examination. Inclusion criteria involved patients with clinically and radiographically diagnosed lung masses, while exclusion criteria included those who did not consent, had contraindications to contrast media, or had conditions such as severe respiratory distress or coagulopathy. The data were analyzed using descriptive statistics, with efficacy measures such as yield and failure rates of CT-guided biopsies and validation measures like sensitivity, specificity, positive predictive value, and negative predictive value for MDCT. The study included 52 patients (50% male, 50% female), aged 16-80, with the most common age group being 51-60 years. Lesions were mainly in the lung parenchyma (65.38%), followed by the mediastinum (15.38%), hilar region (11.54%), and pleura (7.69%). MDCT evaluation classified 84.44% of the lesions as malignant, characterized by irregular contours, inhomogeneous texture, and contrast enhancement, while 15.55% were benign. Histopathology confirmed 42 malignant lesions, with squamous cell carcinoma being the most prevalent. Benign lesions included abscesses, tuberculosis, and pneumonitis. The study achieved a 100% success rate for CT-guided biopsy, with one minor complication (pneumothorax). The diagnostic accuracy of MDCT was notable, with a sensitivity of 100% for detecting malignancies, a specificity of 77.78%, a positive predictive value of 87.50%, and a negative predictive value of 100%, emphasizing its effectiveness in thoracic lesion evaluation. MDCT is a highly effective tool in the evaluation of lung masses, providing critical information that aids in distinguishing between benign and malignant lesions. When combined with CT-guided biopsy, it offers a reliable method for obtaining diagnostic tissue samples, with a high degree of accuracy and a low complication rate. The study underscores the importance of integrating imaging and histopathological findings in the management of thoracic lesions, ultimately enhancing diagnostic precision and informing appropriate clinical interventions.

An intelligent bone age assessment model incorporating multilayer superimposed texture enhancement and the China-05 attention mechanism

In the process of intelligent bone age assessment for Chinese ethnicity, generalized convolutional network models are not well targeted in extracting specific features in medical skeletal images and lack specificity in training and predicting skeletal developmental features in different ethnicities. This study aims to propose a hybrid improved deep residual network model, ZH05-DL-ResNet50, focusing on intelligent bone age assessment for Chinese ethnicity. In the process of building the ZH05-DL-ResNet50 model. First, multiple overlapping texture enhancement layers are introduced through combinatorial optimization, which can better characterize the global features of hand bone radiographs while using less texture information, reducing the interference of redundant information and freeing up computational power; second, the China-05′s spatial focusing mechanism is designed so that the model can intelligently focus on the region of interest, efficiently locate and automatically learn key image information; Finally, the model can be used for intelligent bone age assessment of Chinese ethnicity by constructing a 50-layer residual depth network, the superposition enhancement layer and the focusing mechanism are fused to form the ZH05-DL-ResNet50 model, which mitigates the problem of gradient disappearance and explosion caused by the increase of network depth, and reduces the information loss and depletion of the multi-layer texture superposition features in the focus region of interest. The training was performed using the Chinese ethnographic dataset provided and created by Tsinghua Changgeng Hospital, where 10 % of the radiographs were used as the test set and the rest as the training and validation sets. The overall performance of the models was estimated by comparing the average accuracy and loss values of different models, and the overall performance between model training and model design was evaluated using the data distribution (Distribution) and histogram of bias weight distribution (Histogram). The results showed that the average accuracy of the bone age estimation model was 98.1 % and the average error on the test set was 0.312 years. Evaluation of the model visualization and accuracy comparison showed that the model performed well and was more appropriate and accurate than other bone age estimation models. Therefore, the first intelligent bone age assessment model for Chinese ethnicity, ZH05-DL-ResNet50, was created, which differs from the untargeted nature of other intelligent bone age assessment models, and is used to train and predict hand bone radiographs more appropriately and accurately for Chinese ethnicity.

Protocol for a prospective multicenter randomized controlled trial to evaluate the efficacy of texture and color enhancement imaging (TXI) observation in the detection of colorectal lesions (deTXIon study).

Colonoscopy is the gold standard for detecting and resecting adenomas or early stage cancers to reduce the incidence and mortality rates of colorectal cancer. In a recent observational study, texture and color enhancement imaging (TXI) was reported to improve polyp detection during colonoscopy. This randomized controlled trial involving six Japanese institutions aims to confirm the superiority of TXI over standard white-light imaging (WLI) in detecting colorectal lesions during colonoscopy. During the 1-year study period, 960 patients will be enrolled, with 480 patients in the TXI and WLI groups. The primary endpoint is the mean number of adenomas detected per procedure. The secondary endpoints include adenoma detection rate, advanced adenoma detection rate, polyp detection rate, flat polyp detection rate, depressed lesion detection rate, mean polyps detected per procedure, sessile serrated lesion (SSL) detection rate, mean SSLs detected per procedure and adverse events.

Effect of texture and color enhancement imaging on the visibility of gastric tumors

Texture and color enhancement imaging (TXI) may improve the visibility of gastric tumors and allow their early detection. However, few reports have examined the utility of TXI. Between June 2021 and October 2022, 56 gastric tumors in 51 patients undergoing endoscopic submucosal dissection at Fukuchiyama City Hospital were evaluated preoperatively using conventional white light imaging (WLI), narrow-band imaging (NBI), and TXI modes 1 and 2. The color differences of the tumors and surrounding mucosae were evaluated using the CIE 1976 L*a*b color space, Additionally, the visibility scores were scaled. Of the 56 gastric tumors, 45 were early gastric cancers, and 11 were adenomas. Overall, the color difference in TXI mode 1 was considerably higher compared to WLI (16.36 ± 7.05 vs. 10.84 ± 4.05; p < 0.01). Moreover, the color difference in early gastric cancers was considerably higher in TXI mode 1 compared to WLI, whereas no significant difference was found in adenomas. The visibility score in TXI mode 1 was the highest, and it was significantly higher compared to WLI. Regarding adenomas, the visibility score in TXI mode 1 was also significantly higher compared to that in WLI. TXI may provide improved gastric tumor visibility.

Endoscopic detection and diagnosis of gastric cancer using image-enhanced endoscopy: A systematic review and meta-analysis.

We aimed to conduct a systematic review and meta-analysis to assess the value of image-enhanced endoscopy including blue laser imaging (BLI), linked color imaging, narrow-band imaging (NBI), and texture and color enhancement imaging to detect and diagnose gastric cancer (GC) compared to that of white-light imaging (WLI). Studies meeting the inclusion criteria were identified through PubMed, Cochrane Library, and Japan Medical Abstracts Society databases searches. The pooled risk ratio for dichotomous variables was calculated using the random-effects model to assess the GC detection between WLI and image-enhanced endoscopy. A random-effects model was used to calculate the overall diagnostic performance of WLI and magnifying image-enhanced endoscopy for GC. Sixteen studies met the inclusion criteria. The detection rate of GC was significantly improved in linked color imaging compared with that in WLI (risk ratio, 2.20; 95% confidence interval [CI], 1.39-3.25; p < 0.01) with mild heterogeneity. Magnifying endoscopy with NBI (ME-NBI) obtained a pooled sensitivity, specificity, and area under the summary receiver operating curve of 0.84 (95 % CI, 0.80-0.88), 0.96 (95 % CI, 0.94-0.97), and 0.92, respectively. Similarly, ME-BLI showed a pooled sensitivity, specificity, and area under the curve of 0.81 (95 % CI, 0.77-0.85), 0.85 (95 % CI, 0.82-0.88), and 0.95, respectively. The diagnostic efficacy of ME-NBI/BLI for GC was evidently high compared to that of WLI, However, significant heterogeneity among the NBI studies still existed. Our meta-analysis showed a high detection rate for linked color imagingand a high diagnostic performance of ME-NBI/BLI for GC compared to that with WLI.

MPE-DETR: A multiscale pyramid enhancement network for object detection in low-light images

Object detection has broad applications in areas such as autonomous driving, security surveillance, and deep-sea exploration. However, the performance of current detection algorithms significantly decreases due to the loss of detail, increased noise, and color distortion in images under low-light or nighttime conditions. To address this problem, we propose a plug-and-play multiscale pyramid enhancement network (MPENet), which elegantly cascades with RT-DETR to establish an end-to-end framework for low-light object detection, named MPE-DETR. First, MPENet utilizes Gaussian blur to decompose images into Gaussian pyramids and Laplacian pyramids at different resolutions. Specifically, we designed a high-frequency texture enhancement (HTE) module to capture the edge and texture information of images, and a low-frequency noise smoothing (LNS) module to better understand the overall structure of images and capture global-scale features. Additionally, by concatenating the output features of the HTE and LNS modules along the channel dimension, feature fusion across different scales is realized. We conducted experiments on the ExDark and ExDark + LOD datasets, which are designed for low-light object detection. The results indicate that the proposed method achieved an improvement of 2.1% in mAP@.5 compared to that of existing SOTA models on the ExDark dataset, and demonstrated strong generalizability and robustness on the ExDark + LOD dataset. The code and results are available at https://github.com/PZDJL/MPENet.

Understanding the textural enhancement of low-salt myofibrillar protein gels filled with pea protein pre-emulsions through interfacial behavior: Effects of structural modification and oil phase polarity

This study investigated the effects of pea protein pre-emulsions containing triglyceride- or diglyceride-oil on the emulsifying and gelling properties of low-salt myofibrillar protein (MP). Pea protein isolates treated with pH12-shifting (PPIpH) or ultrasonication (PPIU) demonstrated superior initial interfacial adsorption and higher final interfacial pressure than native pea protein. Within MP/PPI blends, an increased ratio of MP led to a decrease in interfacial pressure, while simultaneously enhancing film elasticity at both polar and non-polar interfaces. Polar diglyceride promoted protein adsorption and fostered interfacial interactions between modified pea proteins and MP, enhancing the cross-linking of transglutaminase (TG) in the composite emulsion gels. Combining diglyceride-type PPIU and PPIpH emulsions with TG increased gel strength to 0.58 N and 0.63 N, respectively, from an initial 0.33 N, yielding a denser protein network with uniformly dispersed oil droplets. Therefore, the utilization of diglyceride and modified PPI can serve as structural enhancers in comminuted meat products.

A two-stage image enhancement and dynamic feature aggregation framework for gastroscopy image segmentation

Accurate and reliable automatic segmentation of lesion areas in gastroscopy images can assist endoscopists in making diagnoses and reduce the possibility of missed or incorrect diagnoses. This paper presents a two-stage framework for segmenting gastroscopy images, which aims to improve the accuracy of medical image segmentation tasks using limited datasets. The proposed framework consists of two stages: the Image Enhancement Stage and the Lesion Segmentation Stage. First, in the Image Enhancement Stage, an image enhancement solution called TDC-Enhance is proposed to enrich the original small-scale gastroscopy image dataset. This solution performs Texture Enhancement, Detail Enhancement, and Color Enhancement on the original images. Then, in the Lesion Segmentation Stage, a multi-path automatic segmentation network for gastroscopy images, named DynaSiam, is introduced. DynaSiam comprises a Dependent Encoder, a Shared Encoder, and a Fusion Decoder. It learns feature information related to the lesion region by encoding the different enhanced images obtained in the Image Enhancement Stage as inputs to the multi-path network. Additionally, a Dynamic Feature Interaction (DFI) block is designed to capture and learn deeper image information, thereby improving the segmentation performance of the model. The experimental results show that the proposed method achieves a 90.80% mIoU, 92.71% Dice coefficient and 96.31% Accuracy. Other performance metrics also indicate the best performance, suggesting that the proposed model has significant potential for clinical analysis and diagnosis. Code and implementation details can be found on GitHub: https://github.com/kyasulee/DynaSiam.

Condyloma acuminatum of the anorectum observed by texture and color enhancement imaging endoscopy and treated by underwater endoscopic mucosal resection

Condyloma acuminatum of the anorectum observed by texture and color enhancement imaging endoscopy and treated by underwater endoscopic mucosal resection

Wettability-Oriented Laser Microgrooving Process on Cemented Carbide Surface.

Surface micro-texture has been shown to enhance wettability and reduce wear on cutting tools. However, there is limited research on how laser parameters impact the dimensional accuracy of surface texture and its wettability. This study focuses on producing arrayed groove textures on WC/Co cemented carbide surfaces using Nd: YAG laser, evaluating the effect of the laser parameters on surface topography and texture accuracy through microscopic observation and simulation. The results indicate that, with laser parameters such as a number of passes less than 5, approximately 16 W power, scanning speed of 100-150 mm/s, and pulse frequency of 30 kHz, the error between the groove width and laser spot diameter was 4.7%. Additionally, the study explores the impact of the groove texture on surface wettability using the solid droplet method and XPS analysis. Comparative experiments reveal that increased surface roughness enhanced oleophobicity, with surfaces exhibiting high texture accuracy and integrity showing improved oleophobic and spreading properties. Thus, the precise regulation of laser processes is crucial for maintaining surface texture integrity and enhancing surface wettability.