TV Model Research Articles

Improving atmospheric pressure vertical correction model using Gaussian function

The Zenith Hydrostatic Delay (ZHD) is essential for high-precision Global Navigation Satellite System (GNSS) and Very Long Baseline Interferometry (VLBI) data processing. Accurate estimation of ZHD relies on in situ atmospheric pressure, which is primarily variable in the vertical direction. Current atmospheric pressure is either site-specific or has limited spatial coverage, necessitating vertical corrections for broader applicability. This study introduces a model that uses a Gaussian function for the vertical correction of atmospheric pressure when in situ meteorological observations are unavailable. Validation with the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) reveals an average Bias and RMS for the new model of 0.31 hPa and 2.96 hPa, respectively. This corresponds to improvements of 37.5% and 80.3% in terms of RMS compared to two commonly used models (T0 and Tv models) that require in situ meteorological observations, respectively. Additional validation with radiosonde data shows an average Bias and RMS of 1.85 hPa and 4.87 hPa, corresponding to the improvement of 42.8% and 71.1% in RMS compared with T0 and Tv models, respectively. These accuracies are sufficient for calculating ZHD to an accuracy of 1 mm by performing atmospheric pressure vertical correction. The new model can correct atmospheric pressure from meteorological stations or numerical weather forecasts to different heights of the troposphere.

An adaptive fractional-order regularization primal-dual image denoising algorithm based on non-convex function

In this paper, a novel non-convex fractional-order image denoising model is proposed to suppress the staircase effect produced by the TV model while maintaining a neat contour. The model combines ℓq(0<q<1) quasi-norm and fractional-order regularization, and employs a diffusion coefficient with a faster convergence rate to preserve more image edges and details. Additionally, an adaptive regularization parameter is designed to adjust the denoising performance of the algorithm. To obtain the optimal approximate solution of the model, an enhanced primal-dual algorithm is adopted and the complexity and convergence of the algorithm are theoretically analyzed. Finally, the effectiveness of the proposed method is demonstrated through numerical experiments.

Hybrid regularization inspired by total variation and deep denoiser prior for image restoration

Image restoration is a fundamental problem in computer vision, with the goal of restoring high-quality images from degraded low-quality observation images. However, the ill-posedness of restoration problem often leads to artifacts in the results. It inspires us to study the combination of prior, effectively restrict the solution space and improving the quality of the restored image. In this paper, a novel hybrid regularization method for image restoration is proposed, which adopts both internal and external image priors into a unified framework. Specifically, a new hybrid regularization model is designed. The TV model and a deep image denoiser are inserted into the restoration model using the plug-and-play framework, protecting image details while implicitly using external information for deep denoising. Moreover, in order to make the proposed hybrid regularization operable, an adaptive parameter method is proposed to adaptively balance the TV model and learned model in iteration. Experiments show that the proposed method surpasses the performance of existing image restoration techniques.

Three-Dimensional Surface Reconstruction from Point Clouds Using Euler’s Elastica Regularization

Euler’s elastica energy regularizer, initially employed in mathematical and physical systems, has recently garnered much attention in image processing and computer vision tasks. Due to the non-convexity, non-smoothness, and high order of its derivative, however, the term has yet to be effectively applied in 3D reconstruction. To this day, the industry is still searching for 3D reconstruction systems that are robust, accurate, efficient, and easy to use. While implicit surface reconstruction methods generally demonstrate superior robustness and flexibility, the traditional methods rely on initialization and can easily become trapped in local minima. Some low-order variational models are able to overcome these issues, but they still struggle with the reconstruction of object details. Euler’s elastica term, on the other hand, has been found to share the advantages of both the TV regularization term and the curvature regularization term. In this paper, we aim to address the problems of missing details and complex computation in implicit 3D reconstruction by efficiently using Euler’s elastica term. The main contributions of this article can be outlined in three aspects. Firstly, Euler’s elastica is introduced as a regularization term in 3D point cloud reconstruction. Secondly, a new dual algorithm is devised for the proposed model, significantly improving solution efficiency compared to the commonly used TV model. Lastly, numerical experiments conducted in 2D and 3D demonstrate the remarkable performance of Euler’s elastica in enhancing features of curved surfaces during point cloud reconstruction. The reconstructed point cloud surface adheres more closely to the initial point cloud surface when compared to the classical TV model. However, it is worth noting that Euler’s elastica exhibits a lesser capability in handling local extrema compared to the TV model.

Mixed Fractional-Order and High-Order Adaptive Image Denoising Algorithm Based on Weight Selection Function

In this paper, a mixed-order image denoising algorithm containing fractional-order and high-order regularization terms is proposed, which effectively suppresses the staircase effect generated by the TV model and its variants while better preserving the edges and details of the image. Adding different regularization penalties in different regions is fundamental to improving the denoising performance of the model. Therefore, a weight selection function is designed using the structure tensor to achieve a more effective selection of regularization terms in different regions. In each iteration, the regularization parameters are adaptively adjusted according to the Morozov discrepancy principle to promote the performance of the algorithm. Based on the primal–dual theory, the original algorithm is improved by using the predictor–corrector scheme to obtain a more accurate approximate solution while ensuring the convergence of the algorithm. The effectiveness of the proposed algorithm is demonstrated through simulation experiments.

Breast ultrasound image classification and physiological assessment based on GoogLeNet

BackgroundMedical ultrasound image classification based on convolutional neural network is the mainstream breast cancer classification model, but its limited perceptual ability limits its ability to obtain global information. MethodologyA total of 880 breast ultrasound images were collected from 700 patients including 103 normal images, 467 malignant tumor images and 210 benign tumor images. In this paper, the diagnosis of breast ultrasound images was realized by constructing the CNN model of GoogLeNet. Firstly, the breast images were preprocessed based on the TV model. After that, the CNN model was trained and a more accurate model with a wider range of application was obtained based on improved Inception. Then we extract image features of different sizes; Then, feature classification was completed in different model classifiers to realize benign and malignant detection of breast cancer. Meanwhile, comparative analysis was performed to verify the excellence of the GoogLeNet model. ResultsThe training time for classification was effectively reduced, and the classification accuracy rate was improved, reaching 96.37% combined with transformer learning. The loss value is down to 0.3492. Then, the classification accuracy of different structural models is discussed in the two models. The results show that GoogLeNet has great advantages in the detection of breast cancer ultrasound images. The influence of migration on experimental results is further discussed. Finally, combined with transfer learning, the three models were tested separately. The results show that transfer learning can improve system performance. ConclusionBased on the TV model and the GoogLeNet model, this paper designs a method of breast cancer detection and classification based on the combination of TV and GoogLeNet model and transfer learning. Experiments show that this method can repair part of the texture damaged by markers in ultrasonic images, effectively extract the features of thyroid nodule, and accurately judge whether the breast is diseased, which greatly improves the diagnostic efficiency of doctors.

An ADMM approach to a TV model for identifying two coefficients in the time-fractional diffusion system

An ADMM approach to a TV model for identifying two coefficients in the time-fractional diffusion system

Illumination Removal via Gaussian Difference L0 Norm Model for Facial Experssion Recognition

Face images in the logarithmic space can be considered as a sum of the texture component and lighting map component according to Lambert Reflection. However, it is still not easy to separate these two parts, because face contour boundaries and lighting change boundaries are difficult to distinguish. In order to enhance the separation quality of these to parts, this paper proposes an illumination standardization algorithm based on extreme L0 Gaussian difference regularization constraints, assuming that illumination is massively spread all over the image but illumination change boundaries are simple, regular, and sparse enough. The proposed algorithm uses an iterative L0 Gaussian difference smoothing method, which achieves a more accurate lighting map estimation by reserving the fewest boundaries. Thus, the texture component of the original image can be restored better by simply subtracting the lighting map estimated. The experiments in this paper are organized with two steps: the first step is to observe the quality of the original texture restoration, and the second step is to test the effectiveness of our algorithm for complex face classification tasks. We choose the facial expression classification in this step. The first step experimental results show that our proposed algorithm can effectively recover face image details from extremely dark or light regions. In the second step experiment, we use a CNN classifier to test the emotion classification accuracy, making a comparison of the proposed illumination removal algorithm and the state-of-the-art illumination removal algorithm as face image preprocessing methods. The experimental results show that our algorithm works best for facial expression classification at about 5 to 7 percent accuracy higher than other algorithms. Therefore, our algorithm is proven to provide effective lighting processing technical support for the complex face classification problems which require a high degree of preservation of facial texture. The contribution of this paper is, first, that this paper proposes an enhanced TV model with an L0 boundary constraint for illumination estimation. Second, the boundary response is formulated with the Gaussian difference, which strongly responds to illumination boundaries. Third, this paper emphasizes the necessity of reserving details for preprocessing face images.

A target-cell limited model can reproduce influenza infection dynamics in hosts with differing immune responses

We consider a hierarchy of ordinary differential equation models that describe the within-host viral kinetics of influenza infections: the IR model explicitly accounts for an immune response to the virus, while the simpler, target-cell limited TEIV and TV models do not. We show that when the IR model is fitted to pooled experimental murine data of the viral load, fraction of dead cells, and immune response levels, its parameters values can be determined. However, if, as is common, only viral load data are available, we can estimate parameters of the TEIV and TV models but not the IR model. These results are substantiated by a structural and practical identifiability analysis. We then use the IR model to generate synthetic data representing infections in hosts whose immune responses differ. We fit the TV model to these synthetic datasets and show that it can reproduce the characteristic exponential increase and decay of viral load generated by the IR model. Furthermore, the values of the fitted parameters of the TV model can be mapped from the immune response parameters in the IR model. We conclude that, if only viral load data are available, a simple target-cell limited model can reproduce influenza infection dynamics and distinguish between hosts with differing immune responses.

Breaking the Televisual Consensus in Chile: Popular Subjectivity and Popular Revolt in the TV Series El reemplazante

AbstractThis article presents a cultural analysis of the Chilean TV series El reemplazante (The substitute). The series ran on TVN (National Television of Chile) from 2012 to 2013, and it is currently available on Netflix. The show broke televisual consensus in Chile that made the production of TV series invested in promoting dissenting political viewpoints virtually impossible. As such, El reemplazante can be conceived as an antecedent of the 2019 social uprising and as an alternative model of critical TV in Chile and Latin America. The article first examines the mediation between the patriarchal and racist model of the superhero teacher and the construction of a popular subjectivity that disrupts said model. It then deals with the hyperrealist aesthetic of the show as a privileged mode of enacting a social critique on TV, and it addresses the representation of the city and its geographies of segregation as a gateway to unpacking the problematic of education in neoliberal Chile. The conclusion reflects on the abrupt cancellation of the show and the limits that the Chilean production model places on TV series with transformative intentions.

Improved TV Image Denoising over Inverse Gradient

Noise in an image can affect one’s extraction of image information, therefore, image denoising is an important image pre-processing process. Many of the existing models have a large number of estimated parameters, which increases the time complexity of the model solution and the achieved denoising effect is less than ideal. As a result, in this paper, an improved image-denoising algorithm is proposed based on the TV model, which effectively solves the above problems. The L1 regularization term can make the solution generated by the model sparser, thus facilitating the recovery of high-quality images. Reducing the number of estimated parameters, while using the inverse gradient to estimate the regularization parameters, enables the parameters to achieve global adaption and improves the denoising effect of the model in combination with the TV regularization term. The split Bregman iteration method is used to decouple the model into several related subproblems, and the solutions of the coordinated subproblems are derived as optimal solutions. It is also shown that the solution of the model converges to a Karush–Kuhn–Tucker point. Experimental results show that the algorithm in this paper is more effective in both preserving image texture structure and suppressing image noise.

A QoE Model for Mulsemedia TV in a Smart Home Environment

The provision to the users of realistic media contents is one of the main goals of future media services. The sense of reality perceived by the user can be enhanced by adding various sensorial effects to the conventional audio-visual content, through the stimulation of the five senses stimulation (sight, hearing, touch, smell and taste), the so-called multi-sensorial media (mulsemedia). To deliver the additional effects within a smart home (SH) environment, custom devices (e.g., air conditioning, lights) providing opportune smart features, are preferred to ad-hoc devices, often deployed in a specific context such as for example in gaming consoles. In the present study, a prototype for a mulsemedia TV application, implemented in a real smart home scenario, allowed the authors to assess the user’s Quality of Experience (QoE) through test measurement campaign. The impact of specific sensory effects (i.e., light, airflow, vibration) on the user experience regarding the enhancement of sense of reality, annoyance, and intensity of the effects was investigated through subjective assessment. The need for multi sensorial QoE models is an important challenge for future research in this field, considering the time and cost of subjective quality assessments. Therefore, based on the subjective assessment results, this paper instantiates and validates a parametric QoE model for multi-sensorial TV in a SH scenario which indicates the relationship between the quality of audiovisual contents and user-perceived QoE for sensory effects applications.

Empirical Pathloss Model Analysis of Television White Space in Lagos, Western Nigeria

To conduct feasible unlicensed communications in the television band, radio equipment must first identify transmission possibilities, or the proportion of the permitted spectrum for broadcasting services that is vacant at a given time in a particular location, which is referred to as Television White Space (TVWS). A drive test was conducted to determine the signal strength of three cell towers in three distinct areas of Lagos state (rural, suburban, and urban). The signal power parameters were measured in rural, urban, and suburban parts of Lagos in order to provide empirical values for the parameters that unlicensed radio devices can utilize to distinguish between vacant and occupied television channels in a real-world scenario. This research presents the results of field measurements in the UHF television band (470-860 MHz) conducted in Lagos. This work investigated and compared several propagation models (COST 231 model, Egli model, Okumura-Hata model, and Plain Earth model). The Standard Deviation Error (SDE), Root Mean Square Error (RMSE), and Mean Error (ME) analysis showed that the COST-231 Hata model is the optimal model for calculating path loss based on path loss exponents. This discovery led to the development of an improved model, the Oressuff TV model, which uses COST-231 Hata parameters to predict path loss in the 470–870 MHz spectrum for rural, urban, and suburban locations. From the result of Root Mean Square Error analysis, the proposed model predicted path loss in rural, urban, and suburban stations with low RMSE of 3.06dB, 3.08dB, and 1.19dB respectively. These figures indicate that the model optimization was successful, and that the Oressuff TV proposed model can estimate the path loss incurred by television signals with greater precision. Telecommunications firms may improve their service by utilizing the proposed model.

Selling Turkish quality: Multiple proximities and Turkish format exports in the post-streaming era

A close analysis of puhutv’s Şahsiyet ( Persona, 2018) and its Mexican adaptation for HBO Max Latin America (HBO Max Latinoamérica), Asesino del Olvido ( Before I Forget, 2021) reveals that the show’s hybridization of the Turkish series’ melodramatic conventions with the Anglo-American quality TV characteristics is the key to its adaptability for Mexican audiences. Melodrama’s domination in both Turkey and Mexico contributes to the similar utilization of melodramatic elements when the Anglo-American quality TV model is adapted for the local audiences. Therefore, the proximity around the mainstream popularity of melodramas in both markets and the shared juxtaposition of melodrama and quality allow Şahsiyet to flow as a quality TV format between Turkey and Mexico.

Subjective video quality for 4K HDR-WCG content using a browser-based approach for &amp;#xC2;&amp;#x201C;at-home&amp;#xC2;&amp;#x201D; testing

The paper describes a design of a subjective experiment for testing the video quality of High Dynamic Range, Wide Color gamut (HDR-WCG) content at 4K resolution. Due to Covid, testing could not use a lab, so an at-home test procedure was developed. To aim for calibration despite not fully controlling the conditions and settings, we limited subjects to those who had a specific TV model, which we had previously calibrated in our labs. Moreover, we performed the experiment in the Dolby Vision mode (where the various enhancements of the TV are turned OFF by default). A browser approach was used which took control of the TV, and ensure the content was viewed at the native resolution of the TV (e.g., dot-on-dot mode). In addition, we know that video imagery is not ergodic, and there is wide variability in types of low levels features (sharpness, noise, motion, color volume, etc.) that affect both TV and visual system performance. So, a large number of test clips was used (30) and the content was specifically chosen to stress key features. The obtained data is qualitatively similar to an in-lab study and is subsequently used to evaluate several existing objective quality metrics.

Simultaneous consideration of relative density, energy consumption, and build time for selective laser melting of Inconel 718: A multi-objective optimization study on process parameter selection

Additive manufacturing (AM) is regarded as sustainability friendly thanks to its significantly reduced production steps and simplified supply chain at the system level, but it is often not energy efficient at the process level. In this study, we propose that the optimization of AM process parameters should not only maximize build quality, but also minimize environmental impact and build time. A multi-objective optimization problem of maximizing relative density (RD), minimizing energy consumption and build time per unit volume (ev and tv respectively) is formulated and solved for selective laser melting (SLM) of Inconel 718 alloy. A high accuracy RD prediction model with R2 of 93.23% is obtained by fitting SLM experiment data from wide ranges of laser power (P), scanning speed (SS), hatch spacing (HS), and layer thickness (LT). The ev and tv models are created considering printing area utilization rate (U) based on laser scanning and powder coating process. The two approaches of Pareto front and global criterion method are adopted for solving the multi-objective optimization problem. It is found that the highest RD of close to 100% can be obtained at the expense of high ev and tv of close to 500 J/mm3 and 0.3 s/mm3, respectively, and about three times ev and tv are needed to increase RD from about 95% to 100% than from 85% to 95%. At the same U level, the solutions obtained from prioritizing ev and tv are consistent, while those from prioritizing RD show smaller process parameter values. The multi-objective optimization formulation provides an enabling tool for industry to obtain high quality SLM-ed parts with improved sustainability and efficiency at the process level. The methodology developed is applicable to selective laser melting of other materials, and such consideration can be readily expanded to other AM processes as well.

Non-convex fractional-order TV model for impulse noise removal

For the pursuit of high-quality restoration effects, we introduce a novel non-convex extended scheme to reconstruct blurred images under impulse noise in this paper. Because of the remarkable advantages of the combination of non-convex ℓp-norm and fractional-order total variation regularization, our developed strategy possesses a brilliant ability to overcome the staircase-like aspects and preserve neat contours. To obtain the minimizer of the optimization problem, we adopt the alternating direction method of multipliers closely integrating with the iteratively reweighted ℓ1 algorithm. Theoretically, a convergence proof is provided for the developed algorithm. By considering salt-and-pepper noise and random-valued impulse noise, comprehensive simulation comparisons with some popular methods are carried out for image restoration. And the experimental results illustrate that our proposed model behaves better with respect to visual comparison and quantitative measurement.

A fractional variational image denoising model with two-component regularization terms

Image denoising is to recover true image from noisy image. Many image deonising models are proposed during the last decades. Some models preserve the margin of tissue, i.e., TV model, while the others, i.e., LLT model, prefer smooth solutions. By decomposing true image into cartoon part and texture part, we propose a fractional image denoising model with two-component regularization terms. Setting some appropriate parameters, the proposed model can deal with both smooth and non-smooth image denosing problems. The existence and uniqueness of solution for the variational model are proved. Moreover, a Split-Bregman(S-B) based numerical algorithm to solve this model is also proposed to validate the theoretical results. Numerical tests show that the proposed model can produce competitive denoising result to the other three published models.

A Validation Study for RANS Based Modelling of Swirling Pulverized Fuel Flames

A swirling pulverized coal flame is computationally investigated. A Eulerian–Lagrangian formulation is used to describe the two-phase flow. Turbulence is modelled within a RANS (Reynolds averaged numerical simulation) framework. Four turbulence viscosity- (TV) based models, namely the standard k-ε model, realizable k-ε model, renormalization group theory k-ε model, and the shear stress transport k-ω model are used. In addition, a Reynolds stress transport model (RSM) is employed. The models are assessed by comparing the predicted velocity fields with the measurements of other authors. In terms of overall average values, the agreement of the predictions to the measurements is observed to be within the range 20–40%. A better performance of the RSM compared to the TV models is observed, with a nearly twice as better overall agreement to the experiments, particularly for the swirl velocity. In the second part of the investigation, the resolution of the discrete particle phase in modelling the turbulent particle dispersion (TPD) and particle size distribution (SD) is investigated. Using the discrete random walk model for the TPD, it is shown that even five random walks are sufficient for an accuracy that is quite high, with a less than 1% mean deviation from the solution obtained by thirty random walks. The approximation of the measured SD is determined by a continuous Rosin–Rammler distribution function, and inaccuracies that can occur in its subsequent discretization are demonstrated and discussed. An investigation on the resolution of the SD by discrete particle size classes (SC) indicates that 12 SC are required for an accuracy with a less than 1% mean deviation from the solution with 18 SC. Although these numbers may not necessarily be claimed to be sufficiently universal, they may serve as guidance, at least for SD with similar characteristics.

Research on Error Concealment for Video Communication

The video sequence restoration described in this article has been extended to many areas of our lives today. People use it to perform various restoration tasks, making it possible for some pictures that were impossible to restore in the past. This paper uses experimental methods to study the error concealment and traditional image restoration in video compression, and mainly studies the CDD model algorithm, TV model algorithm and error mode. This article also has a very important meaning for the related research of video communication error control, that is, the research of digital image restoration technology, in the post-processing of film and television works, the fuzzification of network video, and the restoration of old documentaries.