Image Components Research Articles

Using Hyperspectral Imaging and Principal Component Analysis to Detect and Monitor Water Stress in Ornamental Plants

Water stress is a critical factor affecting the health and productivity of ornamental plants, yet early detection remains challenging. This study aims to investigate the spectral responses of four ornamental plant taxa—Rosa hybrid (rose), Itea virginica (itea), Spiraea nipponica (spirea), and Weigela florida (weigela)—under varying levels of water stress using hyperspectral imaging and principal component analysis (PCA). Hyperspectral data were collected across multiple wavelengths and PCA was applied to identify key spectral bands associated with different stress levels. The analyses revealed that the first two principal components captured a majority of variance in the data, with specific wavelengths around 680 nm, 760 nm, and 810 nm playing a significant role in distinguishing between the stress levels. Score plots demonstrated clear separation between different stress treatments, indicating that spectral signatures evolve distinctly over time as water stress progresses. Influence plots identified observations with disproportionate impacts on the PCA model, ensuring the robustness of the analysis. Findings suggest that hyperspectral imaging, combined with PCA, is a powerful tool for early detection and monitoring of water stress in ornamental plants, providing a basis for improved water management practices in horticulture.

The Effects of Moderate to High Static Magnetic Fields on Pancreatic Damage.

Pancreatic damage is a common digestive system disease with no specific drugs. Static magnetic field (SMF), the key component of magnetic resonance imaging (MRI), has demonstrated prominent effects in various disease models. To study the effects of 0.1-9.4 T SMFs on pancreatic injury induced by alcohol, and acute pancreatitis (AP) induced by L-arginine (L-Arg). Prospective, animal model. Twelve healthy C57BL/6J male mice, 30 AP model male mice, and 30 alcohol-associated liver disease (AALD) model male mice. 1.5-9.4 T SMFs for 12 hours and 0.1 T SMF for 72 hours. No imaging sequence was used. Histological analysis on AALD mice pancreas was conducted. For L-Arg-induced AP mice, their body weight, food/water consumption, open field behavior, blood analysis, as well as histological analysis, inflammatory, oxidative stress of the pancreas were measured. In vitro cellular experiments were also conducted. Data are presented as means ± SD and analyzed using the two-tailed Student's t-test or one-way analysis of variance (ANOVA) test. P values <0.05 were considered statistically significant. 1.5-7 T SMFs significantly reduced alcohol-induced pancreatic damage, increasing the structurally intact acinar area from 51.5% to 78.3%, whereas the effect of 9.4 T SMF is not obvious. 0.1 T SMF can reduce the AP mice lethality, increase the structurally intact acinar area from 31.0% to 59.7%, associated with the reduced pancreatic inflammatory responses (78.1% reduction of F4/80 and 80.0% reduction of MPO), 20.0% decreased oxidative stress and 53.2% reduced pancreatic cell apoptosis. High-field MRI may be safe for pancreatic-related diseases at the animal level. SMFs have a future potential to be developed as non-invasive and highly penetrating physical modalities for the treatment of pancreatic injury including AP. This study aims to evaluate the safety and potential therapeutic effects of moderate- to high-intensity static magnetic fields (SMFs) on mice with pancreatic injury. Their findings revealed that SMFs between 1.5 and 7 Tesla (T) helped reduce alcohol-induced pancreatic damage, while a stronger 9.4 T showed no effect. Interestingly, for mice with L-arginine-induced acute pancreatitis, a weaker 0.1 T significantly increased the area of healthy acinar cells from 31.0% to 59.7%. These results not only suggest that MRI-related SMFs are safe for pancreatic diseases in animals, but also unravel the potential of SMFs as a future treatment option for pancreatic disorders. N/A TECHNICAL EFFICACY: Stage 1.

Salience maps for judgments of frontal plane distance, centroids, numerosity, and letter identity inferred from substance-invariant processing.

A salience map is a topographic map that has inputs at each x,y location from many different feature maps and summarizes the combined salience of all those inputs as a real number, salience, which is represented in the map. Of the more than 1 million Google references to salience maps, nearly all use the map for computing the relative priority of visual image components for subsequent processing. We observe that salience processing is an instance of substance-invariant processing, analogous to household measuring cups, weight scales, and measuring tapes, all of which make single-number substance-invariant measurements. Like these devices, the brain also collects material for substance-invariant measurements but by a different mechanism: salience maps that collect visual substances for subsequent measurement. Each salience map can be used by many different measurements. The instruction to attend is implemented by increasing the salience of the to-be-attended items so they can be collected in a salience map and then further processed. Here we show that, beyond processing priority, the following measurement tasks are substance invariant and therefore use salience maps: computing distance in the frontal plane, computing centroids (center of a cluster of items), computing the numerosity of a collection of items, and identifying alphabetic letters. We painstakingly demonstrate that defining items exclusively by color or texture not only is sufficient for these tasks, but that light-dark luminance information significantly improves performance only for letter recognition. Obviously, visual features are represented in the brain but their salience alone is sufficient for these four judgments.

Aberrant intra-network resting-state functional connectivity in chronic insomnia with or without cognitive impairment

Chronic insomnia (CI) is a common sleep disorder in middle-aged and elderly individuals. Long-term sleep deprivation can lead to physical, mental, and cognitive damage. Resting-state networks (RSNs) in the brain are closely linked to cognition and behavior. Therefore, we investigated changes in RSNs to explore behavioral and cognitive abnormalities in middle-aged and elderly CI patients. Resting state functional magnetic resonance imaging (rs-fMRI) and independent component analysis were used to study the intrinsic functional connectivity (FC) of the RSNs in 36 CI patients (20 CI with cognitive impairment (CI-I) patients and 16 CI without cognitive impairment (CI-N) patients) and 20 healthy controls (HC). Two-sample t-tests were used to compare RSNs differences between CI and HC groups and the RSNs differences between CI-I and CI-N groups. Partial correlation analysis was used to explore the relationship between the significant abnormal brain regions in RSN and clinical scales. Compared with HCs, CI patients showed significant differences in multiple RSNs, and FC values in two brain regions within RSNs were correlated with clinical scales. Furthermore, compared with CI-N group, CI-I group also showed significantly altered FC in multiple RSNs. Moreover, FC values in the right middle frontal gyrus within right frontal parietal network of CI-I patients were negatively correlated with the Mini-Mental State Examination scores. These results may explain hyperarousal, decreased attention and motor function impairments in CI patients. Furthermore, the aberrant alterations of RSNs in CI-I patients may play a crucial role in the onset and progression of cognitive impairment in CI patients.

SAR Image Colorization with a Deep Learning Model for All-Inclusion Understanding (H)

The procedure for enhancing halftone photographs with color to create eye-catching pigmented pictures is known as image colorization. This involves the application of colors in line with the intended purpose of the image. However, traditional methods of colorization available today often include the need for end user’s intervention in the form providing color points and doodle drawings, as well as use of color images for reference in which case colors are transferred to the target image or using multiple color images to predict what the colored result would look like; however, the majority of colorizes developed tend to produce colorized images that are not realistic in most instances due to unskilled users who attempt to apply the devices, wrong color transfer or limited color image library. In this paper, to overcome these weaknesses, a new method that consists of two modules is proposed to colorize certain regions of an image by fusing semantic segmentation and seamless region filling. In the first module, input image foreground and background regions masks and categories are extracted each extracted region's reference image is taken from a per-sorted color image database using a Mask R-CNN model. Using colorization approaches based on the VGG and U-Net models, respectively, the second module entails coloring the image's backdrop and other areas. Afterward, the final, fully colored image is created by combining the photographs using the poission editing process. The tests show that our method enables not only the proper reference image selection based on the semantic information of various image components, but also incorporation of the colored results to create a believable colored image. Proposing to use different CNNbased models in a combination where each is used according to its own strength, In contrast to other methods now in use, our scheme gives a deeper creative visual impression while avoiding the disadvantage of single-step techniques' failure frequency. Keywords: Mask R-CNN, Input Photograph, Image Sections, Coloured Photographs, Conceptual Data, Visuals, Black and White Images, Semantic Pix elation, Reference Picture, Base Image, Images Stockpile, Outlying Areas, Image Library, Segregation Methods, Doodle, Conventional Neural Network Based Designs, U-Net Architecture, One-shot Approach, Strategy Present in This Manuscript, Zonal Regions, Principal Zones, Large Image, Color space, Competitive Half toning Algorithm, Zonal Context, Focus Area, Artistic Incorporation, Partially Automatic Process, Cutting Out Section of the Picture, Instance Level Segmentation

THE IMPACT OF AGROTOURISM DESTINATION IMAGE ON TOURIST’S LOYALTY IN ZAGHOUAN, TUNISIA: MODERATING ROLE OF VISIT’S EXPERIENCE

This study tests the influence of destination image on loyalty. It also examines the moderation effect of visitors’ experiences in the link between the two main components of destination image and loyalty. For this reason, responses from 302 visitors at Zaghouan, Tunisia were collected using a pre-examined questionnaire form. The collected responses were analyzed with SmartPLS SEM and Multi-group analysis. The findings showed that destination image significantly and positively affected visitors’ intention to revisit and recommend agrotourism destination to others. The results confirmed that destination image dimensions (cognitive and affective) influence visitors’ loyalty. These influences are significantly moderated by tourist’s experiences. New visitors with new experiences have less influence on the link between image and loyalty than regular visitors. The findings have some propositions for tourism destination marketers and scholars.

FEATURES OF LITERARY TRANSLATION

The aim of this paper is to determine the phenomenon of artistic translation. In modern philological science, there are various directions of studying the speech characteristics of characters as the primary component of a character's image in a literary work. Contemporary literary studies consider the speech characteristics of characters as one of the aspects of creating characters in literary texts of different genres. In linguistics, the subject of study is based on the material of oral speech and texts that present general, typological, and specific features of communication, as well as the specifics of the language of different social groups. Recently, there has been a growing interest in issues related to reproducing characters' speech characteristics in literary translations. The need for further study of the features of creating speech characteristics and their adequate transfer to the target language underscores the relevance of this research. The results of the analysis shed light on the most common types of translation transformations for conveying speech characteristics, allowing conclusions to be drawn regarding the norms of the target culture.

Low-Light Image Enhancement Integrating Retinex-Inspired Extended Decomposition with a Plug-and-Play Framework

Images captured under low-light conditions often suffer from serious degradation due to insufficient light, which adversely impacts subsequent computer vision tasks. Retinex-based methods have demonstrated strong potential in low-light image enhancement. However, existing approaches often directly design prior regularization functions for either illumination or reflectance components, which may unintentionally introduce noise. To address these limitations, this paper presents an enhancement method by integrating a Plug-and-Play strategy into an extended decomposition model. The proposed model consists of three main components: an extended decomposition term, an iterative reweighting regularization function for the illumination component, and a Plug-and-Play refinement term applied to the reflectance component. The extended decomposition enables a more precise representation of image components, while the iterative reweighting mechanism allows for gentle smoothing near edges and brighter areas while applying more pronounced smoothing in darker regions. Additionally, the Plug-and-Play framework incorporates off-the-shelf image denoising filters to effectively suppress noise and preserve useful image details. Extensive experiments on several datasets confirm that the proposed method consistently outperforms existing techniques.

Satellite image encryption using amalgamation of randomized three chaotic maps and DNA encoding

Abstract Image encryption is essential for securing sensitive visual data, protecting privacy, and making certain that only authorized users are able to access the required content. It prevents unauthorized access, tampering, and misuse of images, which is crucial for confidential and secure communications. In fields like healthcare, military, satellite communication, and financial services, encrypted images safeguard against data breaches and cyber threats. As digital images are easily shared and stored, encryption contributes to the integrity maintenance, and privacy of visual data. Motivated by these requirements, we have developed a satellite image encryption scheme that employs a novel 1-D Cosine Sinusoidal Chaotic (1DCSC) map, and two earlier proposed Sine-Tangent Chaotic (STC) and Improved Cosine Fractional Chaotic (ICFCM) maps, in conjunction with Deoxyribonucleic Acid (DNA) operations. The proposed scheme encrypts a given satellite image in four steps. In the initial step, three keys (K1, K2, and K3) are created utilizing a 384-bit shared key and a 128-bit initial vector. In step two, three different chaotic sequences are produced using 1DCSC, STC, and ICFCM maps, which are chosen randomly to encrypt three red, blue or green different components of the given input image. In step three, these three chaotic sequences and the three components of the input image are DNA encoded. In the final step, the green, red, and blue cipher images are created by employing the DNA XOR based diffusion operation between the color components of the DNA-encoded image and DNA encoded chaotic sequences. The proposed scheme obtains entropy value 7.9997, Unified Average Changing Intensity (UACI) value 33.32, and Number of Pixels Change Rate (NPCR) value 99.67.&amp;#xD;

MCIDN: Deblurring Network for Metal Corrosion Images

The analysis of corrosion images is crucial in materials science, where acquiring clear images is fundamental for subsequent analysis. The goal of deblurring metal corrosion images is to reconstruct clear images from degraded ones. To the best of our knowledge, this study introduces the first paired blurry-sharp image dataset specifically designed for the metal corrosion domain, filling a critical gap in the existing research. This innovative approach effectively addresses the unique challenges associated with deblurring metal corrosion images. We propose a novel metal corrosion images deblurring network (MCIDN) that employs a dual-domain attention mechanism, integrating both spatial and frequency domains to enhance image clarity. This innovative approach effectively addresses the unique challenges associated with deblurring metal corrosion images. While self-attention is widely used in visual tasks, its quadratic complexity often leads to high computational costs. To address this issue, we introduce a new spatial channel attention module (SCAM) that employs dynamic group convolutions to achieve self-attention, effectively integrating information from local regions and enhancing representation learning capabilities. Recognizing the critical role of frequency components in image restoration, we develop a frequency channel attention module (FCAM) that selectively focuses on different frequency components of images, thereby enhancing deblurring performance. These two attention modules are seamlessly integrated into our network. Compared to existing methods, our approach demonstrates superior performance on datasets of blurry metal corrosion images, achieving a peak signal-to-noise ratio (PSNR) of 32.8645 dB and a structural similarity (SSIM) of 0.9768. These metrics indicate that our method provides clearer and more detailed reconstructions, significantly enhancing the image quality.

Медиаобраз Арктики: решение задачи кадрового обеспечения региона

The article is devoted to the relevant topic — the Russian Arctic labor resources. The potential labor force of the Arctic today includes representatives of generation Z, graduates of Russian universities. The strategic issue of attracting labor resources to the Arctic has a target audience of young people. The governmental policy agent is mass media. The media provide public narratives of social constructs. The media are used to represent such an important social construct for the state as the Russian Arctic. The established media image of the Arctic includes characteristics related to the harsh weather conditions, the region’s resource endowment, its military potential, and environmental problems. The traditional components of the media image of the Arctic have little significance in terms of highlighting the region’s human resource potential. The existing media image of the Arctic is uninformative for potential Arctic labor force due to the features of public information consumption and processing by young people. The content of the media image has a specific pragmatic component that is directly related to the issue of labor vacancies, living conditions, work and lifestyle in the location of the vacancy. The aim of the study is to determine the media image of the Arctic corresponding to the Russian government’s strategy for the development of the area. As a result, recommendations are made to adjust the media image of the Arctic to solve the problem of staffing the Arctic. The scientific novelty of the study is in the data obtained through surveys of Arctic university students. This data allows us to assess the degree of young people’s awareness of the possibilities for applying professional competencies in the development of the Arctic. The study of the modern youth features, including the character of media consumption, also constitutes scientific novelty in the study of tools for the formation of Arctic workforce. Practical significance is represented by the possibilities that will provide a new media image of the Arctic for the creation of human resources potential of the AZRF.

Multivariantism of Auditory Perceptions as a Significant Element of the Auditory Scene Analysis Concept

The concept of auditory scene analysis, popularized in scientific experiments by A. S. Bregman, the primary architect of the perceptual streaming theory, and his research team, along with more recent analyses by subsequent researchers, highlights a specific scientific gap that has not been thoroughly explored in previous studies. This article seeks to expand on this concept by introducing the author’s observation of the multivariant nature of auditory perception. This notion suggests that listeners focusing on different components of an auditory image (such as a musical piece) may perceive the same sounds but interpret them as distinct sound structures. Notably, even the same listener may perceive various structures (different mental figures) when re-listening to the same piece, depending on which musical elements they focus on. The thesis of multivariantism was examined and confirmed through the analysis of selected classical music pieces, providing concrete evidence of different interpretations of the same sound stimuli. To enhance clarity and understanding, the introduction to multivariantism was supplemented with graphic examples from the visual arts, which were then related to musical art through score excerpts from the works of composers such as C. Saint-Saëns, F. Liszt, and F. Mendelssohn Bartholdy.

Brand Image in Cosmetic Packaging Design with a Visual Appeal Approach (Case Study: MS Glow)

The study identified the brand image of the cosmetic packaging moisturizer MS Glow. MS Glows is a local cosmetics brand in Indonesia, one of its best products is Moisturizers which won the first championship as the best local moisturizer of the year 2022. Qualitative descriptive methods with observation techniques, literature, and documentation are used in this research. The theoretical framework used included a visual appeal approach (Kotler and Amstrong) and brand image (Keller). The results showed that the moisturizer packaging of three variants (yuzu, watermelon, cactus, and grape seed) consists of primary, secondary, and tertiary packaging. Practical shapes, lightweight materials, colors in harmony with product variants, a combination of serif and sans serif letters, illustrations highlighting the main materials, and an asymmetrical layout. Three brand image components, namely the strength of the brand association, the benefits of its association, and the uniqueness of brand associations evaluate the ideal and accurate design of the MS Glow moisturizer packaging so that products spread across the market can be trusted. This study shows that an ideal visual appeal can build relevant packaging and open up opportunities for MS Glow to create products with their distinctive image.

Backlight Imaging Based on Laser-Gated Technology

Backlight imaging refers to the process of capturing images when the light source directly enters the lens of imaging devices or against a high-brightness background, which usually suffers from degraded imaging quality caused by direct or reflected strong light. Traditional backlight imaging methods involve reducing light flux, expanding dynamic range, and utilizing avoidance angles. However, these methods only partially address the issue of backlighting, and are unable to effectively extract information from the areas overwhelmed by the backlight. To overcome these limitations, this paper reported a backlight imaging technique based on active illumination laser gated imaging technology (AILGIT), originally applied in underwater scattering imaging. Given that backlight imaging is essentially a form of scattering imaging, this technique is likely applicable to backlight scenarios. The AILGIT employs nanosecond-gated imaging components synchronized with nanosecond pulse laser illumination to spatially slice the target. This method allows the camera to capture target signals within specific slices only, which effectively suppresses ambient light and scattering interference from the medium and achieves high-contrast imaging with strong backlight suppression. Experiments obtained dynamic backlight imaging results for a vehicle with headlight on at night from a distance of 500 m, with 60 frames per second and a 4.2 by 2.8 meters’ field of view, where wheel contours and the license plate can be clearly distinguished. The result not only demonstrates the potential of AILGIT in suppressing strong backlight, but also lays the foundation for further research on laser 3D imaging and subsequent processing techniques for backlight targets.

DESRGAN: Detail-enhanced generative adversarial networks for small sample single image super-resolution

Image super-resolution involves reconstructing a blurry, low-resolution image with limited information into a clear, high-resolution image containing more detailed information. The images generated by super-resolution reconstruction can enhance the performance of downstream computer vision tasks, and hold wide application prospects in fields such as industrial fault detection, plant phenotype parameter extraction, medical imaging, and more. High-frequency components in images, such as edges and texture details, typically require more attention. However, when the training samples are limited, effectively recovering clear high-frequency details of images becomes highly challenging. Therefore, this paper proposes a single-image super-resolution method based on generative adversarial networks, named DESRGAN. Compared to existing methods, DESRGAN achieves better reconstruction of image details even with a limited number of training samples. DESRGAN introduces several key innovations: a shallow generator structure to address overfitting issues in small sample scenarios, a dual-stream feature extraction network with dilated convolutions to capture multi-scale contextual information and expand the receptive field, and an artifact loss designed to eliminate artifacts and preserve the true high-frequency details of the super-resolved images. Extensive ablation experiments and comparative studies with multiple state-of-the-art models are conducted on two small sample datasets, "Root" and "Leaves," as well as five publicly available datasets. The results demonstrate that the proposed DESRGAN achieves superior performance in small sample single image super-resolution tasks, with improvements of 1.39 dB in PSNR and 0.013 in SSIM. The generated high-resolution images exhibit clear texture and edge structures, presenting favorable subjective visual effects. Moreover, the model displays strong generalization capabilities.

The Environmental Impact of Iodinated Contrast Media: Strategies for Optimized Use and Recycling.

Iodinated contrast media (ICM) is an integral and ubiquitous component of modern diagnostic imaging. Although most radiology practices are familiar with ICM administration and physiological excretion, they may be less aware of how much ICM is wasted on a per exam basis. Furthermore, radiologists may not recognize the environmental fate of discarded ICM waste. In an evolving world where medical practices are increasingly cognizant of their environmental footprint and radiology practices are considered high consumers of resources, it behooves the radiology community to understand the ICM lifecycle and ways to mitigate unnecessary waste. This review article explains the origin and environmental fate of discarded ICM, with special focus on wastewater contamination. Secondly, the article focuses on feasible options to both optimize use and decrease consumable waste. Specifically, the article addresses ICM vial size inventory diversification, multi-use ICM vials, syringeless contrast injectors, and the potential for using multi-energy imaging (dual-energy or photon counting CT) to accomplish these goals. Finally, the authors share their institutional experience participating in an ICM recycling program and its current departmental impact.

Role of Image Segmentation and Deep Learning in Medical Imaging

The rapid advancements in medical imaging technologies have significantly enhanced diagnostic accuracy and clinical decision-making in modern healthcare. Image segmentation and deep learning have emerged as transformative tools among these advancements. This article explores the pivotal role of image segmentation and deep learning in medical imaging, detailing their methodologies, applications, challenges, and future directions. Deep learning, particularly Convolutional Neural Networks (CNNs), has revolutionized medical imaging by automating the analysis of complex datasets and improving diagnostic precision. Image segmentation, a fundamental component of medical imaging, allows for delineating specific structures such as organs, tissues, and pathological regions. Together, these technologies have been applied in diverse fields, including oncology, cardiology, neurology, and ophthalmology, enabling applications such as tumor detection, organ segmentation, disease progression monitoring, and treatment planning. However, despite its transformative potential, the integration of deep learning into medical imaging faces several challenges. These include data scarcity, privacy concerns, interpretability issues, and regulatory hurdles. The article discusses various strategies to address these challenges, such as data augmentation, transfer learning, and the development of explainable AI models to ensure transparency and trustworthiness. Evaluation metrics, such as accuracy, sensitivity, specificity, and Dice Similarity Coefficient (DSC), are essential for assessing model performance. Rigorous clinical validation and regulatory approval are crucial to integrating deep learning systems into clinical workflows effectively. Looking ahead, the future of deep learning in medical imaging holds immense promise. Innovations like multimodal imaging, personalized medicine, and AI-driven automation are set to further revolutionize the field, enhancing the efficiency and accuracy of diagnostics. Collaborative efforts between clinicians, researchers, and AI developers will play a vital role in overcoming current limitations and driving progress. This article concludes by emphasizing the transformative potential of deep learning and image segmentation in medical imaging, highlighting their ability to improve diagnostic accuracy, streamline clinical workflows, and ultimately, enhance patient care. By addressing current challenges and continuing to innovate, these technologies are poised to redefine the landscape of medical diagnostics and treatment in the years to come.

Age Difference on the Spatial Scale of Object Categorization

ABSTRACT Background As the elderly population is growing worldwide and communication is increasingly relayed by visual interfaces, identifying age-related changes in the visual perception of complex stimuli is critical. We examined the effect of spatial frequency filtering on object categorization in young (mean 25 years) and older (mean 65 years) participants. Method The stimuli used were low spatial frequency (LSF, cutoff 8 cpi) or high spatial frequency (HSF, cutoff 24 cpi) images of objects of various categories, and hybrid images composed of a LSF object superimposed on a HSF object from a different semantic category. Participants were asked to press a key when they detected an object from a specified category (e.g., animal). Results Young participants categorized efficiently LSF and HSF images, and exhibited a bias towards the HSF component of hybrid images. Older participants showed a better performance on LSF than on HSF images and exhibited a strong bias towards the LSF component of hybrid image. Conclusion The results show that LSF are better preserved than HSF in older people. The greater interference of LSF than HSF in older than in young adults suggests that LSF are available earlier than HSF in older people.

Flexible disk ultramicroelectrode: Facile preparation and high-resolution scanning electrochemical microscopy imaging

BackgroundScanning electrochemical microscopy (SECM) is a kind of scanning probe technology that enables the obtainment of surface morphology and electrochemical information by recording changes in Faraday current triggered by the movement of probe. ResultsIn this work, flexible disk ultramicroelectrode (UME) with highly repeatable geometry are fabricated through a simple and universal strategy that involves vacuum pulling the glass capillaries inserted with platinum wire (gold wire, carbon fiber, etc.), followed by a rapidly heated sealing and polishing process. Based on the optical and electrochemical characterization, the as-prepared UMEs possess favorable geometric shape with a small RG value from 1.7 to 2. Consequently, when applied as probe components for SECM imaging on interdigital electrodes, it can achieve excellent spatial resolution and high sensitivity at short working distances. Moreover, due to the outstanding flexibility, the as-prepared UMEs can be minimally damaged arising from tip-sample collision and produce invisible trace on apple peel after the contact scanning, which is far superior to the traditional UME and suggests the application potential in bio-related fields. SignificanceOur work will inspire the application of flexible UMEs as probe components in SECM, and consequently promote the widespread use of SECM with reduced collision threshold and low operational difficulty.

An efficient method for image denoising based on a new nonlinear wavelet thresholding function

Image noise is random variation of brightness or color information in images, and is usually an aspect of electronic noise. It can be produced by the image sensor and circuitry of a scanner or digital camera. In fact, there are different kinds of noise functions such as Gaussian noise, salt and pepper noise and speckle noise. Hence, image denoising is the process of removing noise from an image using one of the denoising methods such as spatial or transform techniques. The main aim of an image denoising technique is to achieve both noise reduction and feature preservation. In this context, the wavelet denoising method works in the transform domain, where the noise is uniformly spread throughout coefficients while most of the image information is concentrated in a few large ones.Therefore, the first wavelet-based denoising methods were based on thresholding of detail subband coefficients. However, most of the wavelet thresholding methods suffer from the drawback that the chosen threshold may not match the specific distribution of the image and noise components. Another thing: Studies have proven that the thresholding function has an effective role in obtaining better quality of the denoised image. To address these disadvantages, in this paper, we propose an efficient method for image denoising in wavelets domain. This method is based on a new nonlinear wavelet thresholding function, that is characterized by main mathematical properties and a shape parameter. The theoretical study of this new method proves that we can overcome the drawbacks of classical thresholding methods and by freely adjusting the shape parameter, we achieve a compromise between Hard and Soft thresholding. The experimental results show that our proposed method provides better performance compared to classical thresholding methods in terms of the visual quality of the denoised image.