Picture Quality Research Articles

Self-steering Yagi-Uda antenna positioning system for television

The aim of this study is to develop a prototype that automatically improves the position of a Yagi-Uda antenna using a microcontroller and to illustrate its radiation pattern through the use of MATLAB<sup>®</sup>. This study is intended for students and professors in the electronics engineering field. This served as their educational materials for teaching antenna system principles and theories. Developmental and experimental methods were used to achieve the objectives. The materials and components generally used in this study are a Yagi-Uda antenna, stepper motor, Arduino Uno, L293D motor shield, USB TV stick tuner, slotted optocoupler, ADS1115, coax cable splitter, speaker stand, and timing belt. The statistical tool used in this study was a Z-test to find out if the experiment results were significant. In testing the effectiveness of the automatic antenna system, the TV display in every increment of 1.8° was taken. It was the basis for the effectiveness of the study. At 5% α/2 level (1.96), the computed z value is 1.76, which is less than 1.96. Therefore, there is no significant difference between the picture quality of the TV display at every angle and the desired angle with maximum reception of the signal with the integration of MATLAB<sup>®</sup>.

Public Perception of Digital Switchover of Television Broadcasting in Delta State

The study examines public perception of the level of digital switch over in both public and private television broadcasting in Delta State. The researcher used survey research method to determine the sample size of 400 through the appropriate statistical method to represent the population of the study. Survey research method was employed in the collection of data because it was easier to seek people’s opinion using questionnaire. Data gathered from the study were analyzed and interpreted using simple percentage and tables. Findings from the research show that digitization will help improve accessibility, sharp picture quality, good and efficient programming. This research has revealed that not all broadcast stations in Delta State are not fully digitized; this is because of some constraints that militate against the media house such as lack of adequate funds to run the broadcast station properly. Privately owned media stations in Delta State whose major problems are lack of adequate funds/ finance according to findings, should be provided with adequate funds through the Federal annual budget allocation. Government should provide the broadcast station its full monthly subvention to fully operate maximally like other mass media organizations, because of the essential services it renders to the public.

Mathematical analysis of wavelet-based multi-image compression in medical diagnostics

Accurate diagnosis and well-informed treatment choices are only possible with the help of sophisticated medical imaging technology. The introduction of digital imaging methods has led to a dramatic rise in the volume of data associated with medical images, which in turn has led to difficulties in their storage, transmission, and administration. It is crucial to utilize effective image compression techniques to address these challenges without compromising the diagnostic integrity of the pictures. The wavelet transform has matured into a potent method for striking a good compromise between picture quality and file size reduction while compressing. The safe and efficient transfer of medical image data is a major concern in today’s healthcare settings. In this academic investigation, we investigate how wavelet transform-based techniques may be used to enhance medical picture compression. The proposed techniques optimize compression ratios while maintaining diagnostic picture quality by making use of the wavelet transform’s multi-resolution and frequency localization features. To address the unique challenges given by medical image collections, various iterations of the wavelet transform and compression techniques are investigated. Through a series of detailed tests involving several medical picture modalities, the effectiveness of these technologies is thoroughly evaluated, demonstrating their effectiveness in achieving significant data reduction without sacrificing clinical information.

A Method of Tactile Resistive Sensor Array Calibration

Resistive sensor arrays (RSA) comprising of pressure-sensitive elements find broad use in pressure-sensing applications. However, sensor manufacturing inevitably yields a number of non-ideal properties. The gain, offset or non-linearities of an RSA’s elements may vary between individual sensors (taxels). Hence a simple calibration procedure to suppress these effects is desirable. The paper presents a method of and apparatus for the calibration of a pressure-sensing RSA. A forward mathematical model for each sensor is obtained during the calibration phase. In subsequent online measurement, the inverse model is utilised to compensated for the non-uniformity in sensor gain and offset. This approach leads to an appreciable improvement in picture quality, which can be mathematically quantified using classification accuracy in our tactile anti-decubitus platform for human monitoring. Namely, in a four-class classification experiment involving a support vector machine, the classification error decreased from 11 % before calibration to 3.5 % after calibration. Owing to the calibration procedure, a classifier trained using a calibrated RSA can be deployed to another calibrated RSA, without further data collection.

Investigating the Impact of Compressed Sensing Techniques and IoT in Medical Imaging

This research paper examines compressed sensing's impact on medical imaging. Math and signal processing inspired compressed sensing. Future picture-capturing will be radically different. The paper focuses on adaptive random sampling (ARS), iterative shrinkage-thresholding algorithms (ISTA), and temporal compressed sensing (TCS). These approaches were rigorously tested using MRIs, X-rays, and dynamic imaging patterns. Low scan times, picture quality, and dynamic imaging were the main test criteria. The technologies considerably reduced scan time, demonstrating their potential to speed up imaging procedures. The reconstructed photos had higher SNRs and SSIs than those obtained using normal techniques, indicating greater accuracy. The TCS algorithm's dynamic imaging skills, especially evident in heart and musculoskeletal imaging, eliminated motion defects while exhibiting real-time physiological changes. The study was expanded to incorporate customized treatment, and the recommended procedures have proven amazing adaptability to each patient's demands. This adaptability fits current medical treatments, making unique imaging technologies viable.

Adaptive dermascopy application using machine learning

Skin cancer is the most lethal because skin cells develop abnormally. Finding skin cancer early is very important and may help stop some kinds of skin cancer, like melanoma and focal cell carcinoma. Early detection and classification of skin cancer are difficult and costly. Recurrent networks and ConvNets can automatically extract complex data. This paper proposes to use a handmade features-based multi-layer perceptron and a cascaded ensembled network to upgrade ConvNet models. This convolutional neural network model detects non-handmade picture qualities and generates features like color moments and material properties. With ensembled DL, accuracy increased from 85.3% with convolutional neural networks to 98.3%.

Image reconstruction techniques using deep learning quality segmentation

Translational CT (TCT), in developing nations, a low-end computed tomography (CT) technology are relatively common. The limited-angle TCT scanning mode is often used with large-angle scanning to scan items within a narrow angular range, reduce X-ray radiation, scan long objects, and prevent detector discrepancies.. However, this scanning mode greatly reduces the picture quality and diagnostic accuracy due to the added noise and limited-angle distortions. A U-net convolutional neural network-based approach for limited-angle TCT image reconstruction has been created to reconstruct a high-quality image for the limited-angle TCT scanning mode (CNN). The limited-angle TCT projection data are first examined using the SART method, and the resulting picture is then fed into a trained CNN that can reduce artifacts and maintain structures to provide a better reconstructed image. Simulated studies are used to demonstrate the effectiveness of the algorithm designed for the limitedangle TCT scanning mode. In contrast to certain modern techniques, the developed algorithm considerably lowers noise and limited-angle artifacts while maintaining image structures.

Enhancing breast cancer detection from histopathology images: A novel ensemble approach with deep learning-based feature extraction

Effective detection and diagnostic procedures are necessary to enhance patient results for the common and life-threatening illness of breast cancer. Current approaches have limits in scalability and efficiency, highlighting the need for more study. This work introduces a hybrid Breast Cancer (BC) detecting approach that merges Deep Learning (DL) with pre-trained modeling of Histopathology Images (HPI) and an ensemble-based Machine Learning (ML) approach. DL integration allows learning and identifying hidden trends in intricate BC pictures, while ML techniques provide interpretability and generalization skills. Contrast Limited Adaptive Histogram Equalization (CLAHE) was used on HPI as a pre-processing technique to improve picture quality. The ResNet50V2 model was used for deep feature extraction. The Ensemble Learning (EL) model combines predictions from four basic ML approaches using soft voting. The research attained a superior accuracy, precision, recall, and F1 score compared to the most advanced models. This study provides substantial advancements in breast cancer diagnosis, thorough performance evaluation, and reliable assessment. Furthermore, it helps medical personnel make well-informed choices, enhance patient care, and improve results for BC sufferers.

Enhancing steganography using modified HHO and RC4

A lot of information is being sent around the globe these days, which increases the risk of knowledge leakage; as a result, it is crucial to preserve information security while exchanging data. Steganography and cryptography are the most essential methods, among many others, to guarantee the data's confidentiality. Encryption is making information unintelligible to anybody but the intended recipients. However, steganography is the study of hiding information in digital media so that nobody can detect it is there. This research introduces two methods for concealing and encrypting messages that improve the Stego image's visual quality and security while offering a sizable embedding capacity. A stream cipher method with a symmetric key, the RC4 algorithm, is utilized to encrypt and decode secret information. At the same time, a population-based metaheuristic, modified HHO, is employed to find the optimum spots in the cover medium to conceal encrypted sensitive data. Regarding security, picture quality, and attack resistance, the testing findings show that the proposed method excels over competing methods in terms of time and cost.

Multi-Mode Vector Light Field Generation Using Modified Off-Axis Interferometric Holography and Liquid Crystal Spatial Light Modulators

The increasing enhancement in the modulation accuracy of spatial light modulators has garnered significant attention towards real-time control technology for light fields based on these modulators. It has been demonstrated that this technology possesses a remarkable capability to generate vector beams with arbitrary complex amplitude distributions. Nevertheless, past studies indicate that the generation of only one vector beam at a time has been observed. The simultaneous generation of numerous vector light fields can give rise to several challenges, including compromised picture quality, limited single-mode operation, and intricate optical path configurations. In pursuit of this objective, we present a novel methodology that integrates the coding methodology of modified off-axis interferometric holography with the idea of optical superposition. This technique facilitates the concurrent generation of several vector beams. In this study, we present a demonstration of the simultaneous creation of twelve vector beams using a single spatial light modulator (SLM) as a proof of concept. Significantly, this technology has the ability to generate an unlimited quantity of vector light fields concurrently under the assumption that the resolution of the SLM does not impose any limitations. The findings indicate that the imaging quality achieved by this technology is of a high standard. Furthermore, it is possible to separately control the beam waist radius, topological charge, polarization order, and extra phase of each beam.

Strategi Komunikasi KPI (Komisi Penyiaran Informasi) dalam Mensosialisasikan TV Digital

The aim of this research is to introduce the public to the digitalization of broadcasting, especially television in Indonesia and DKI Jakarta in particular. Currently we are entering the era of digitalization of broadcasting marked by ASO. For this reason, KPID DKI Jakarta is holding this JBeX activity, one aim is to provide sufficient information to the public regarding digitalization of broadcasting. Because usually when we talk about digital, people only know about digital internet. The method used is descriptive qualitative research with case studies. The information collection method used includes in-depth interviews and obtaining other information such as documents and other data sources related to this research. Based on the results of the research that has been carried out, the researchers then held a discussion regarding the results of the data obtained regarding KPI communication strategies in socializing Digital TV. In this research, researchers used persuasive theory and Lasswell's communication model. The result was that the Indonesian Broadcasting Commission built additional towers to strengthen the signal. This is proven by the large number of people who switch to digital TV channels rather than other TV channels. The Indonesian Broadcasting Commission also verifies digital TV data in maximizing broadcast signals. These improvements make the picture and sound quality much better compared to analog TV broadcasts.

Melissopalynology Characterization of Pakistani Honey

The aim of the study was to add latest scientific information regarding bee flora through pollen analysis available for honeybees in different districts of Punjab province, Pakistan. A total of 50 honey samples, natural (n=32) and branded (n=18) were collected and examined as per the method recommended by International Honey Commission. Natural honey samples were collected from beekeepers. Pollens of families Poacea (26.5%), Azadirachta spp. (22%), Citrus spp. (17.45%), Pisum spp. (16.41%), Ziziphus spp. (13.99%), Prosopis spp. (13.13%), Brassica spp. (8.57%), Malvacea (8.08%), Syzygium spp. (7.29%), Cassia spp. (6.2%), Acacia spp. (5.17%) and Eucalyptus spp. (4.35%) were common in both branded and fresh honeys. Whereas Morus spp. (8.5%), Moringa spp. (4.46%), Psidium spp. (4.23%), Bombax spp. (1.9%), Mangifera spp. (1.9%) were found in fresh honeys only. Similarly, four different types of Pollens (Melilotus spp. (8.6%), Alfa spp. (6.4%), Benincasa spp. (6.4%) and Halianthus spp. (4.3%)) were detected exclusively in various branded honeys Muqeet (n=4), Sary (n=2), Swat honey, Marhaba (n=3), Youngs (Beehive), Ubqari, Salman (Pak honey) Al-Shifa, Ponam, Langanase and Aftab Qarshi. There was a correlation (r=0.24) between pollens of same taxa and families in branded and fresh honeys. High quality pictures were taken by camera fitted on light microscope.

Online Depiction of Urticaria Is Often Flawed and Does Not Reflect the Spectrum of Clinical Manifestation

Introduction: The internet is a popular source of health information including images of disease manifestations. Online photographs of skin lesions may aid patients in identifying their disease, if these pictures are of good quality and of the disease they claim to show. If not, patients may be at risk of delayed diagnosis, misdiagnosis, and suboptimal treatment. For urticaria, the mismatch rate and quality of online pictures are unknown. The objective of this study was therefore to evaluate the content and quality of online images of urticaria. Methods: The search term “urticaria” was applied to Google Images and Shutterstock. The top 100 photographs from each search engine were retrieved on October 9th, 2022. Illustrations, drawings, and heavily edited photographs were excluded. Each image was evaluated for patient characteristics, characteristics of urticarial lesions, and image quality. Results: Across 194 unique images of urticaria (after removing duplicates), 35 (18.0%) did not depict urticarial lesions, and 38 (19.6%) were ambiguous. Less than two-thirds of images 121 (62.4%) showed bona fide urticarial lesions. Pictures of urticarial lesions under-represented children and did not reflect female preponderance of the disease. Images predominantly depicted urticaria lesions on Caucasian skin (59.8%) and were typical of spontaneous rather than inducible urticaria. Only 3 (1.5%) pictures showed angioedema, a common clinical sign in patients with urticaria. The overall quality of online urticaria pictures was mostly good or very good. Conclusion: Physicians and patients should be aware that one in five online pictures of urticaria does not show urticarial skin lesions, and children, females, non-Caucasian patients, inducible urticaria, and angioedema are under-represented. These findings should prompt efforts to improve the accuracy and representativeness of online urticaria pictures.

Machine learning applied to hydro generator stator bars quality control

In order to improve visual inspections carried out by quality control in the manufacture of hydro generator stator bars, as well as agreeing high levels of requirements from hydroelectric plant customers regarding the proper functioning of their machine components, is created the hypothesis that the use of machine learning would enable a semi-automatic tool to support visual inspection, which involves subjective decisions that depend entirely on the level of experience and commitment of its executor. The focus of the article is on the use of the Edge Impulse platform to develop the tool, since the software proves to be easy to use, provides several resources and provides access to people with low knowledge in the area of information technology due to its low level of requirement for programming languages and data processing. It was proposed to use the DOE (design of experiments) tool to analyze the parameters and observe the factors that influence the model final result. Graphics presented during the text help to understand how the quality, quantity and model of pictures influence machine learning. With that, in addition to obtaining the results, there was also a more comprehensive perspective on the Edge Impulse software.

Analysis of the Teaching Path of Animation Film Majors Integrating Digital Visual Space in the Context of Digital Media Art

Abstract In this study, we first adopt the dynamic stereo vision spatial coordinate measurement method of non-zoom cameras, establish a coordinate measurement model based on differential GPS, and study the application of the spatial coordinates of dynamic stereo digital vision systems in animation design. Secondly, the internal and external parameters of the camera are defined as the static error of the dynamic stereo vision system so as to provide an easy and quick-to-use design tool for the teaching of animation film majors. Finally, a test sequence animation sample set is used to verify the teaching effect of digital visual space based on animation design. The results show that in the multi-comparison test of animation quality against pixel bit rate, the animation design samples were found to have frame rates in the range of 5-30 Fps. And under the condition of having extra bandwidth, the animation feature learning evaluation dimension is 2589, and increasing the animation visual frame rate can improve the output picture quality of animation more effectively. It shows that the integration of digital visual space in the context of digital media art can allow students to have enough project sources for learning and practicing so as to improve their professional ability.

Spatial enhancement of Landsat-9 land surface temperature imagery by Fourier transformation-based panchromatic fusion

ABSTRACT Landsat-9 Panchromatic (PAN) band images are 7 times finer than land surface temperature (LST) photos of the Thermal Infrared (TIR) band. PAN bands have superior image resolution, consistency, and less ambiguity than TIR bands due to their smaller pixel sizes. Image fusion enhances images by combining data from several sources to make them better. Image fusion methods cannot combine PAN and TIR bands. This research proposes Fourier Transformation-based fusion (FTBF) to merge PAN and TIR band data to spatially enhance Landsat-9 LST images from 100 m to 15 m resolution. Fourier transformation integrates frequency domain filtering and spatial matching in FTBF. In-situ infrared thermometers data loggers verified temperature and picture quality parameters for FTBF algorithm fused image thermal points. Comparing downscaled LST with ground truth points yielded an RMSE of 0.18 and a correlation of 0.93. Eight qualitative and quantitative characteristics reveal that FTBF fusion methods improve TIR picture spatial resolution and preserve original LST data thermal attributes. LST-Pan fusion can detect surface temperature change for land-use change, fire detection, forest fire, agricultural analysis, crop management, and flood mapping at finer scales.

A Deep Learning-Based Multidimensional Aesthetic Quality Assessment Method for Mobile Game Images

Mobile games have played an increasingly significant role in people's leisure lives in recent years, thanks to the fast expansion of the gaming industry and the widespread use of mobile devices. The aesthetic quality of game pictures is a very important factor that attracts users' interest. However, evaluating the aesthetic quality of mobile game pictures is difficult since the painting styles of games vary greatly and the evaluation criteria are also diversified. In this paper, we propose a multi-task deep learning based method, which is able to predict the aesthetic quality of mobile game images in multiple dimensions (MGQA). The proposed model consists of two modules, a feature extraction module and a quality regression module. We extract quality-aware features from intermediate layers of the deep convolution neural network (CNN) and then incorporate them into the final feature representation in the feature extraction module, allowing the model to fully use visual information from low to high levels. The quality regression module uses fully connected (FC) layers to map quality-aware features into quality scores across multiple dimensions. The multi-dimensional aesthetic quality scores are trained using a multi-task learning approach, in which quality-aware features are shared across multiple dimensional quality prediction tasks. Finally, several key factors which help the proposed model perform better are analyzed. The experimental results indicate that our proposed method not only achieves the greatest performance on mobile game images, but also is applicable to natural scene images.

En plein air: Light, space and fashion in Filippo de Pisis’s aesthetics

This article addresses the concept of en plein air regarding men’s fashion in early twentieth-century Italy. The act of painting outdoors has been addressed at length in the field of art history as one of the most radical practices of nineteenth-century avantgardes. It focused on the changing appearance of light and weather conditions, as in the case of the renowned French Impressionist painters, who often represented women and their clothing in the natural environment. However, en plein air can also provide an opportunity to reframe men’s fashion in its relationship with the environment. The Italian painter and dandy Filippo de Pisis (1896–56) relied on en plein air to constitute his peculiar vision of ‘pictorial elegance’, connecting painting skills with the experience of wearing clothes in open spaces, for instance, during a stroll in the Mediterranean sunshine. This article analyses de Pisis’s artworks and writings on male elegance and its pictorial qualities, aiming to explore the under-investigated invisibility of men’s fashion in the early twentieth century. By doing so, it challenges the misogyny of the antifashion rhetoric of Modernist aesthetics. The case of de Pisis is particularly interesting in this regard because it provides an illuminating pictorial and textual affective interpretation of the interdependences between men’s bodies, the colours and fabrics of their clothes and the qualities of the open-air environment in which they were experienced. This contribution intends to shed light on the concept of ‘pictorial elegance’ that was for de Pisis a queer, destabilizing and poetic transfiguration of masculinity in the context of European Modernism.

Multilevel thresholding image segmentation algorithm based on Mumford–Shah model

Abstract Image segmentation is one of the important tasks of computer vision and computer image processing, and the purpose of image segmentation is to achieve the extraction and recognition of the target image region. The classical Mumford–Shah (MSh) image segmentation model is used to achieve the segmentation of images. With the goal to get the best segmentation effect on images by minimizing the MSh energy generalization function, a level set strategy is developed, and a model with global information infinite curve evolution is utilized. However, considering the low efficiency of this model for processing level set curves and the general quality of image segmentation. A multi-layer threshold search scheme is proposed to achieve rapid convergence of the target image level set curve. The experimental results showed that the multi-level thresholding image segmentation algorithm based on the MSh model can significantly improve the segmentation effect of images and reduce the segmentation time. The suggested MSK method outperforms the MPO algorithm, SSA algorithm, and EMO algorithm in the picture segmentation convergence time test, respectively, in terms of runtime efficiency by 356, 289, and 71%. Additionally, it performs superbly in both threshold searches and picture quality tests. The research topic has significant reference value for the study of contemporary computer vision imaging technologies.

Hybrid Deep Convolutional Generative Adversarial Network (DCGAN) and Xtreme Gradient Boost for X-ray Image Augmentation and Detection

The COVID-19 pandemic has exerted a widespread influence on a global scale, leading numerous nations to prepare for the endemicity of COVID-19. The polymerase chain reaction (PCR) swab test has emerged as the prevailing technique for identifying viral infections within the current pandemic. Following this, the application of chest X-ray imaging in individuals provides an alternate approach for evaluating the existence of viral infection. However, it is imperative to further boost the quality of collected chest pictures via additional data augmentation. The aim of this paper is to provide a technique for the automated analysis of X-ray pictures using server processing with a deep convolutional generative adversarial network (DCGAN). The proposed methodology aims to improve the overall image quality of X-ray scans. The integration of deep learning with Xtreme Gradient Boosting in the DCGAN technique aims to improve the quality of X-ray pictures processed on the server. The training model employed in this work is based on the Inception V3 learning model, which is combined with XGradient Boost. The results obtained from the training procedure were quite interesting: the training model had an accuracy rate of 98.86%, a sensitivity score of 99.1%, and a recall rate of 98.7%.