Texture Enhancement Research Articles

The use of choline chloride for salt reduction and texture enhancement in bread

Cereal products, and in particular bread, are the main source of salt in human diets. However, salt is a critical ingredient in breadmaking, and its reduction can have a negative impact on bread quality. In order to respond to the World Health Organization (WHO)’s recommendation to reduce dietary salt intake, this research aims to develop a bread recipe "reduced" in salt (sodium chloride or SC) by -50 % and partly substituted by choline chloride (CC). The effect of CC on the properties of bread was studied during baking, cooling and storage of bread. It was observed that partially substituting SC with CC led to an improvement in bread texture. Indeed, CC bread was technologically similar to the control bread. In aqueous solution, CC behaves like an ionic liquid and causes a reorganization of the internal structure of the starch grain. As a result, the staling study highlighted the positive effect of CC on bread texture and on the starch retrogradation. CC limited amylopectin recrystallization by reducing water availability in the medium, consequently slowing down the staling of the bread.

Texture and color enhancement imaging improves the visibility of gastric neoplasms: clinical trial with image catalogue assessment using conventional and newly developed endoscopes

BackgroundTexture and color enhancement imaging (TXI) enhances the changes in endoscopic features caused by gastric neoplasms, such as redness/whiteness and elevation/depression. This study aimed to demonstrate the effectiveness of TXI in improving the visibility of gastric neoplasms compared with white light imaging (WLI) using conventional (CE) and newly developed endoscopes (NE).MethodsWe recruited patients who were histologically diagnosed with gastric neoplasms; endoscopy was performed, and gastric neoplasms photographed using three imaging modalities, including WLI, TXI mode 1 (TXI-1) and TXI mode 2 (TXI-2). Two different endoscopes (CE and NE) were used for the same patients. Six endoscopists provided the visibility scale scores ranging from 1 (poor) to 4 (excellent) for gastric neoplasms. The primary outcome was the visibility scale scores based on each modality and endoscope. The secondary outcome was the identification of factors including H. pylori infection, atrophy, location, size, morphology, histological diagnosis and intestinal metaplasia that affect the differences in visibility scale scores between TXI-1/TXI-2 and WLI.ResultsFifty-two gastric neoplasms were analyzed. The mean visibility scale scores with the NE were 2.79 ± 1.07, 3.23 ± 0.96 and 3.14 ± 0.92 for WLI, TXI-1 and TXI-2, respectively. The mean visibility scales with the CE were 2.53 ± 1.10, 3.04 ± 1.05 and 2.96 ± 1.92 for WLI, TXI-1 and TXI-2, respectively. For both endoscopes, significant differences were observed in visibility scale scores between WLI and TXI-1 (p < 0.001) and between WLI and TXI-2 (p < 0.001). The visibility scale scores of NE were superior to those of CE in all modalities. In the secondary outcome, there was no factor affected the differences of visibility scale scores between TXI-1/TXI-2 and WLI.ConclusionsThis study demonstrated that TXI-1 and TXI-2 enhanced the visibility scale scores of gastric neoplasms compared with that of WLI. Moreover, newly developed endoscope has the potential to improve visibility compared to conventional endoscope.Trial RegistrationThis study was registered with the University Hospital Medical Information Network (UMIN000042429, 16/11/2020).

Exposing low-quality deepfake videos of Social Network Service using Spatial Restored Detection Framework

The increasing abuse of facial manipulation methods, such as FaceSwap, Deepfakes etc., seriously threatens the authenticity of digital images/videos on the Internet. Therefore, it is of great importance to identify the facial videos to confirm the contents and avoid fake news or rumours. Many researchers have paid great attention to the detection of deepfakes and put forward a number of deep-learning-based detection models.The existing approaches mostly face the performance degradation in detecting low-quality(LQ) videos, i.e. heavily compressed or low-resolution videos through some SNS (Social Network Service), resulting in the limitation in real applications. To address this issue, in this paper, a novel Spatial Restore Detection Framework(SRDF) is proposed for improving the detection performance for LQ videos by restoring spatial features. We designed a feature extraction-enhancement block and a mapping block inspired by super-resolution methods, to restore and enhance texture features. An attention module was introduced to guide the texture features restoration and enhancement stage attending to different local areas and restoring the texture features. Besides, an improved isolated loss was put forward to prevent the expansion of a single area concerned. Moreover, we adopted a regional data augmentation strategy to prompt feature restore and enhancement in the region attended. Extensive experiments conducted on two deepfake datasets have validated the superiority of the proposed method compared to the state-of-the-art, especially in the scenarios of detecting low-quality deepfake videos.

Dog nose-print recognition based on the shape and spatial features of scales

This study proposes a novel method for dog identification using nose-print recognition, with applications in controlling stray dogs, locating lost pets, and pet insurance verification. Nose prints offer a unique and safer means of recognition than implanted chips. Accurate positioning of the dog's nose print (DNP) region and comparing its features can enhance recognition accuracy. The two-stage segmentation method effectively segments dog nostrils and nose boundaries in the DNP image. It combines brightness and texture enhancement with the U-Net model to generate a dog nose-print mask (DNPMask) and extracts scale-like features using a genetic algorithm. Experiments comparing one-stage and two-stage segmentation methods demonstrate the latter's superiority, with higher recall, precision, accuracy, and F1 score values. The DNPMask method achieves an average recognition rate of 94.93%, 97.10%, and 97.10% for Top1, Top2, and Top3, respectively, significantly improving dog recognition accuracy. However, its performance may be affected by poor-quality images.Nevertheless, the proposed dog recognition method effectively identifies most dog identities and holds promise for real-life applications, enhancing overall accuracy. The research findings provide a valuable reference for developing dog recognition systems and offer new ideas and directions for related research fields. The improved performance of dog nose-print recognition based on scale-like shape and spatial features (DNPISSFS) supports its superiority over Texture Feature-based Dog Noseprint Image Matching (TFDNPIM), suggesting enhanced dog recognition capabilities with broader implications in animal tracking, pet management, and wildlife conservation. Further analysis and experimentation on a larger dataset will ascertain the method's generalizability and robustness, addressing potential challenges and limitations for future research and practical implementation in various domains. The source code and trained models are publicly available at: https://github.com/Chuen-HorngLin/Texture-Feature-based-Dog-Noseprint-Image-Matching.

Underwater image enhancement method based on golden jackal optimization

The propagation of light underwater is affected by water absorption and scattering, resulting in poor visibility of obtained underwater images, and exhibiting degradation phenomena such as color deviation and low contrast. To improve the quality and visibility of underwater images, a method of underwater image enhancement based on Golden Jackal optimization is proposed. The algorithm consists of two processing stages. The first stage is mainly processed in the RGB color space. Based on the attenuation characteristics of underwater color channels, a constructed color compensation and correction strategy is used to eliminate color deviation caused by water absorption. In second stage, it is mainly processed in the Lab color space, using the Golden Jackal optimization algorithm and fitness function to search the optimal parameters of transformation function to improve the image contrast by optimizing the grey intensity of L component and its histogram distribution. The feasibility and effectiveness of the proposed method have been verified through testing and analysis of much underwater degraded image data. In subjective and objective analysis with multiple state-of-the-art underwater image clarity methods, the proposed method performs excellent in color restoration and detail texture enhancement. Further experiments have shown that the proposed method has strong robustness and universality. It is not only suitable for enhancing various underwater scene images, but also for enhancing environmental images with fog, low lighting, and sandstorms. Meanwhile, the proposed method can effectively improve the performance of underwater image segmentation, keypoint detection, and saliency detection.

Texture and Color Enhancement Imaging Improves Colonic Adenoma Detection: A Multicenter Randomized Controlled Trial

The global burden of colorectal cancer is expected to increase more than 60% by 2030; however, compelling evidence now shows that the implementation of population screening programs in developed countries has led to a substantial reduction in incidence and mortality.1,2 Despite this, patients continue to develop preventable colorectal cancers, in part because of high rates of interval colon cancer diagnosed after screening or surveillance colonoscopies.3.

Additional 30-Second Observation of the Right-Sided Colon for Missed Polyp Detection With Texture and Color Enhancement Imaging Compared with Narrow Band Imaging: A Randomized Trial.

The efficacy of texture and color enhancement imaging (TXI) in the novel light-emitting diode endoscopic system for polyp detection has not been examined. We aimed to evaluate the noninferiority of the additional 30-second (Add-30-s) observation of the right-sided colon (cecum/ascending colon) with TXI compared with narrow band imaging (NBI) for detecting missed polyps. We enrolled 381 patients ≥40 years old who underwent colonoscopy from September 2021 to June 2022 in 3 institutions and randomly assigned them to either the TXI or NBI groups. The right-sided colon was first observed with white light imaging in both groups. Second, after reinsertion from hepatic flexure to the cecum, the right-sided colon was observed with Add-30-s observation of either TXI or NBI. The primary endpoint was to examine the noninferiority of TXI to NBI using the mean number of adenomas and sessile serrated lesions per patient. The secondary ones were to examine adenoma detection rate, adenoma and sessile serrated lesions detection rates, and polyp detection rates in both groups. The TXI and NBI groups consisted of 177 and 181 patients, respectively, and the noninferiorities of the mean number of adenomas and sessile serrated lesions per patients in the second observation were significant (TXI 0.29 [51/177] vs NBI 0.30 [54/181], P < 0.01). The change in adenoma detection rate, adenoma and sessile serrated lesions detection rate, and polyp detection rate for the right-sided colon between the TXI and NBI groups were not different (10.2%/10.5% [ P = 0.81], 13.0%/12.7% [ P = 0.71], and 15.3%/13.8% [ P = 0.71]), respectively. Regarding Add-30-s observation of the right-sided colon, TXI was noninferior to NBI.

The protective effects of black tea as nitrite replacer on the oxidation, physicochemical and sensory properties of steamed beef

Having in mind the potential hazard of nitrite as carcinogens and its extensive use in realistic meat preservation, a natural substitute of nitrite was expected to be explored from the massive antioxidants contained in black tea. This study investigated the effect of black tea infusion (BTI) on proximate composition, texture, surface color, sensory, antioxidant activity, lipid oxidation, protein oxidation and microbial quality of steamed beef. Steamed beef was prepared with the treatment of BTI (1%, 2%, 3%, 4% and 5%), and nitrite (0.01%), respectively. Compared to the control group, beef treated with both BTI and nitrite showed significantly improved texture characteristics, decreased L* values, higher a* values, increased antioxidant activities as well as enhanced resistance to lipid and protein oxidation. In addition, the optimal value of acceptability for BTI-treated beef was 4%. Beef with BTI was more stable in color than beef with nitrite. Based on the above results, BTI possessed as positive function as nitrite on the texture and flavor enhancement, oxidation prevention of steamed beef. Hence, our finding provides theoretical support for the application of BTI as natural replacer for nitrite in maintaining beef quality and freshness.

MURF: Mutually Reinforcing Multi-Modal Image Registration and Fusion.

Existing image fusion methods are typically limited to aligned source images and have to "tolerate" parallaxes when images are unaligned. Simultaneously, the large variances between different modalities pose a significant challenge for multi-modal image registration. This study proposes a novel method called MURF, where for the first time, image registration and fusion are mutually reinforced rather than being treated as separate issues. MURF leverages three modules: shared information extraction module (SIEM), multi-scale coarse registration module (MCRM), and fine registration and fusion module (F2M). The registration is carried out in a coarse-to-fine manner. During coarse registration, SIEM first transforms multi-modal images into mono-modal shared information to eliminate the modal variances. Then, MCRM progressively corrects the global rigid parallaxes. Subsequently, fine registration to repair local non-rigid offsets and image fusion are uniformly implemented in F2M. The fused image provides feedback to improve registration accuracy, and the improved registration result further improves the fusion result. For image fusion, rather than solely preserving the original source information in existing methods, we attempt to incorporate texture enhancement into image fusion. We test on four types of multi-modal data (RGB-IR, RGB-NIR, PET-MRI, and CT-MRI). Extensive registration and fusion results validate the superiority and universality of MURF.

Pinstripe Pattern of Esophageal Achalasia Demonstrated With Texture and Color Enhancement Imaging

Pinstripe Pattern of Esophageal Achalasia Demonstrated With Texture and Color Enhancement Imaging

Revolutionizing goat milk gels: A central composite design approach for synthesizing ascorbic acid-functionalized iron oxide nanoparticles decorated alginate-chitosan nanoparticles fortified smart gels

Goat milk gels (GMGs) are popular food due to their high water content, low-calorie density, appealing taste, texture enhancers, stability, and satiety-enhancing characteristics, making them ideal for achieving food security and zero hunger. The GMGs were optimized using the central composite design matrix of response surface methodology using goat milk powder (35–55 g), whole milk powder (10–25 g), and potato powder (10–15 g) as independent variables. In contrast, complex modulus, flow stress, and forward extrudability were chosen as dependent variables. The maximum value of complex modulus 33670.9 N, good flow stress 7863.6 N, and good extrudability 65.32 N was achieved under optimal conditions. The optimized goat milk gel was fortified with ascorbic acid-coated iron oxide nanoparticle (magnetic nature) decorated alginate-chitosan nanoparticles (AA-MNP@CANPs), making it nutritionally rich in an economically feasible way—the decorated AA-MNP@CANPs characterized for size, shape, crystallinity, surface charge, and optical characteristics. Finally, the optimized fortified smart GMGs were further characterized via Scanning electron microscopy, Rheology, Texture profile analysis, Fourier transforms infrared (FTIR), and X-Ray Diffraction (XRD). The fortified smart GMGs carry more nutritional diversity, targeted iron delivery, and the fundamental sustainability development goal of food security.

Cholangioscopy in IgG4-related sclerosing cholangitis using texture and color enhancement imaging and red dichromatic imaging.

Cholangioscopy in IgG4-related sclerosing cholangitis using texture and color enhancement imaging and red dichromatic imaging.

Higher detectability of gastric cancer after Helicobacter pylori eradication in texture and color enhancement imaging mode 2 in screening endoscopy.

The utility of texture and color enhancement imaging (TXI) in detecting gastric cancer (GC) has been investigated. However, few reports exist on TXI mode2 (TXI2) used for detecting GC; this study investigated the efficacy of TXI2 in GC detection during screening endoscopy. This study enrolled 13,440 participants with confirmed Helicobacter pylori (H. pylori) infection status who underwent screening endoscopy by 20 endoscopists in our health screening center. The participants were divided into two groups: one group was observed using white light imaging (WLI) only by 17 endoscopists (WLI group, 10,745 participants), and the other group was observed using TXI2 only by the other three endoscopists (TXI2 group, 2695 participants). We analyzed the detection rate and the characteristics of GC. In addition, considering the bias due to the diagnostic ability, we analyzed the subset of the WLI group where the participants were evaluated by the top three endoscopists based on their GC detection rate (Expert-WLI group, 2792 participants) for comparison with the TXI2 group. Fifty patients were diagnosed with GC. The GC detection rates were 0.68% and 0.71% in the Expert-WLI and TXI2 groups, respectively. In patients who underwent screening endoscopy after H. pylori eradication, the detection rates of differentiated GC, L-region lesions, and surface depressed-type lesions were 0.52%, 0%, and 0.43% in the Expert-WLI group and 1.36%, 0.78%, and 1.36% in the TXI2 group, respectively. In screening endoscopy, the detectability of differentiated GC and L-region lesions and surface depressed-type lesions after H. pylori eradication was higher in TXI2.

Detecting colorectal lesions with image-enhanced endoscopy: an updated review from clinical trials.

Colonoscopy plays an important role in reducing the incidence and mortality of colorectal cancer by detecting adenomas and other precancerous lesions. Image-enhanced endoscopy (IEE) increases lesion visibility by enhancing the microstructure, blood vessels, and mucosal surface color, resulting in the detection of colorectal lesions. In recent years, various IEE techniques have been used in clinical practice, each with its unique characteristics. Numerous studies have reported the effectiveness of IEE in the detection of colorectal lesions. IEEs can be divided into two broad categories according to the nature of the image: images constructed using narrowband wavelength light, such as narrowband imaging and blue laser imaging/blue light imaging, or color images based on white light, such as linked color imaging, texture and color enhancement imaging, and i-scan. Conversely, artificial intelligence (AI) systems, such as computer-aided diagnosis systems, have recently been developed to assist endoscopists in detecting colorectal lesions during colonoscopy. To better understand the features of each IEE, this review presents the effectiveness of each type of IEE and their combination with AI for colorectal lesion detection by referencing the latest research data.

DMVC: Decomposed Motion Modeling for Learned Video Compression

Inter prediction is the critical component in hybrid coding framework to deal with the temporal redundancy. Most of the neural video coding methods typically follow the motion compensation based inter coding scheme, establishing motion vector (MV) as the central role. In this paper, we innovatively propose an efficient motion modeling approach by inherently decomposing it into two components, the intrinsic motion and the compensatory motion. The intrinsic motion originates from the implicit spatiotemporal context hidden in the historical sequence, which can be intuitively captured free of bits. On the top of it, the compensatory motion acts a role of structural refinement and texture enhancement as a form of side information. In particular, the inter prediction is performed in the feature space as a manner of progressive temporal transition, conditioned on the decomposed motion. By the motion decomposition paradigm, we innovatively answer the question of motion representation, compensation and coding in the learned video compression framework. With the temporal prediction, the remaining pixel residue is signaled to obtain the reconstruction. Extensive experimental results demonstrate that the proposed method achieves state-of-the-art coding performance on par with other end-to-end coding methods, and outperforms versatile video coding (VVC) under low-delay P (LDP) configuration in terms of MS-SSIM metric.

Image enhanced colonoscopy: updates and prospects-a review.

Colonoscopy has been proven to be a successful approach in both identifying and preventing colorectal cancer. The incorporation of advanced imaging technologies, such as image-enhanced endoscopy (IEE), plays a vital role in real-time diagnosis. The advancements in endoscopic imaging technology have been continuous, from replacing fiber optics with charge-coupled devices to the introduction of chromoendoscopy in the 1970s. Recent technological advancements include "push-button" technologies like autofluorescence imaging (AFI), narrowed-spectrum endoscopy, and confocal laser endomicroscopy (CLE). Dye-based chromoendoscopy (DCE) is falling out of favor due to the longer time required for application and removal of the dye and the difficulty of identifying lesions in certain situations. Narrow band imaging (NBI) is a technology that filters the light used for illumination leading to improved contrast and better visibility of structures on the mucosal surface and has shown a consistently higher adenoma detection rate (ADR) compared to white light endoscopy. CLE has high sensitivity and specificity for polyp detection and characterization, and several classifications have been developed for accurate identification of normal, regenerative, and dysplastic epithelium. Other IEE technologies, such as blue laser imaging (BLI), linked-color imaging (LCI), i-SCAN, and AFI, have also shown promise in improving ADR and characterizing polyps. New technologies, such as Optivista, red dichromatic imaging (RDI), texture and color enhancement imaging (TXI), and computer-aided detection (CAD) using artificial intelligence (AI), are being developed to improve polyp detection and pathology prediction prior to widespread use in clinical practice.

Global attention-enabled texture enhancement network for MR image reconstruction.

Although recent convolutional neural network (CNN) methodologies have shown promising results in fast MR imaging, there is still a desire to explore how they can be used to learn the frequency characteristics of multicontrast images and reconstruct texture details. A global attention-enabled texture enhancement network (GATE-Net) with a frequency-dependent feature extraction module (FDFEM) and convolution-based global attention module (GAM) is proposed to address the highly under-sampling MR image reconstruction problem. First, FDFEM enables GATE-Net to effectively extract high-frequency features from shareable information of multicontrast images to improve the texture details of reconstructed images. Second, GAM with less computation complexity has the receptive field of the entire image, which can fully explore useful shareable information of multi-contrast images and suppress less beneficial shareable information. The ablation studies are conducted to evaluate the effectiveness of the proposed FDFEM and GAM. Experimental results under various acceleration rates and datasets consistently demonstrate the superiority of GATE-Net, in terms of peak signal-to-noise ratio, structural similarity and normalized mean square error. A global attention-enabled texture enhancement network is proposed. it can be applied to multicontrast MR image reconstruction tasks with different acceleration rates and datasets and achieves superior performance in comparison with state-of-the-art methods.

Usefulness of texture and color enhancement imaging in the endoscopic management of duodenal ampullary tumors.

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Nanocrystalline Nd–Fe–B Anisotropic Magnets by Flash Spark Plasma Sintering

Flash spark plasma sintering (flash SPS) is an attractive method to obtain Nd–Fe–B magnets with anisotropic magnetic properties when starting from melt‐spun powders. Compared to the benchmark processing route via hot pressing with subsequent die upsetting, flash SPS promises electroplasticity as an additional deformation mechanism and reduced tool wear, while maximizing magnetic properties by tailoring the microstructure—fully dense and high texture. A detailed parameter study is conducted to understand the influence of Flash SPS parameters on the densification and magnetic properties of commercial MQU‐F powder. It is revealed that the presintering conditions and preheating temperature before applying the power pulse play a major role for tailoring grain size and texture in the case of hot deformation via Flash SPS. Detailed microstructure and magnetic domain evaluation disclose the texture enhancement with increasing flash SPS temperature at the expense of coercivity. The best compromise between remanence and coercivity (1.37 T and 1195 kA m−1, respectively) is achieved through a combination of presintering at 500 °C for 120 s and preheating temperature of 600 °C, resulting in a magnet with energy product (BH)max of 350 kJm−3. These findings show the potential of flash SPS to obtain fully dense anisotropic nanocrystalline magnets with high magnetic performance.

Effect of strain rate on the microstructure evolution and superplastic deformation behavior of cold-rolled medium Mn lightweight steel

This work investigates the high-temperature tensile behavior of a Medium Mn lightweight steel (MMLS) with a chemical composition of Fe-0.3C-7.0Mn-3.5Al. The MMLS was prepared by hot-rolling followed by cold-rolling to produce a complete martensite microstructure with a high density of dislocations. The tensile tests were conducted at 800 °C within the strain rate range of 5 × 10−4 s−1-2.5 × 10−2 s−1 to explore the effect of microstructural evolution and deformation mechanism by strain rates during the tensile deformation. The results showed that the deformation behavior and mechanism of the MMLS depend not only on the initial grain size and morphology but also on the microstructure evolution during deformation. The MMLS exhibits high strain rate superplasticity (HSRS) behavior, and the deformation mechanisms are dependent on the strain rate. At lower strain rate (5 × 10−4 s−1), dislocation slip/creep is more pronounced in the γ-phase, and grain boundary sliding (GBS) is inhibited during the early deformation period, contributing to the significant texture enhancement. At moderate rate (5 × 10−3 s−1), GBS dominates deformation, while at high strain rate (2.5 × 10−2 s−1), dislocation activity and dynamic recrystallization (DRX) play a significant role. The HSRS behavior of MMLS is related to the dynamic reverse phase transition from cold-rolled ferrite to austenitic/ferrite fine-grained dual-phase structure. This work provides a theoretical basis for the development of superplastic MMLS with improved HSRS behavior and controlled the microstructure/mechanical property of component parts.