High Degree Of Self-similarity Research Articles

Image Restoration using Nonlocal Regularized Variational Model with Spatially Adapted Regularization Parameter

Restoration of blurred images corrupted with impulse noise has attracted increasing attention due to its great potential toward practical applications. To guarantee the noisy image deblurring performance, total variation (TV), one of the most popular regularizers, has been extensively exploited in the literature. This local regularizer is known for its capability of preserving edges and penalizing oscillations but not sharp discontinuities. However, TV regularized variational models commonly suffer from undesirable staircase-like artifacts in homogeneous regions. The reason behind this phenomenon is that TV regularizer favors solutions that are piecewise constant in numerical experiments. To overcome the model-dependent deficiency, we proposed to replace the commonly used local regularizer by its nonlocal extension, i.e., nonlocal total variation (NLTV) regularizer. By taking advantage of the high degree of self-similarity within images, the proposed nonlocal variational model is able to remove both blurring and impulse noise while preserving meaningful image details. To guarantee solution stability and efficiency, the resulting nonsmooth image restoration problem was effectively dealt with using an alternating optimization algorithm. In addition, to further enhance the image quality, a local variance estimator-based automatic method was introduced to calculate the spatially adapted regularization parameters during image restoration. Extensive experiments have demonstrated that the proposed method could achieve superior imaging performance compared with several state-of-the-art image restoration methods.

Error Concealment in the Density Field of a Spatiotemporal Image Sequence.

One of the most difficult challenges of multimedia transmission during the last two decades has been the retrieval of degraded or missing regions of images and videos while maintaining satisfactory perceptual accuracy. The objective is to retrieve lost data by using the similarity between frames. Usually, error concealment (EC) schemes depend on replacing incorrect data with data that are identical to the initial. This is possible because video contains a high degree of self-similarity. This research focuses on applying an EC approach in transform-domain video sequences. To conduct EC on films, they must first be translated to frames and then transformed using one of the available transformations into frequency-domain images. Using successive frames, it is possible to recover lost or incorrect data from images. Intra-coded frames (I-frames) may be used to recreate lost knowledge in predictive (P-frames) and bidirectional predictive frames (B-frames). I-frame knowledge that has been lost may be restored using previous intra-coded frames. The use of wavelet error concealment generated more precise results than the other techniques. In this study, it was discovered that covering faults in the density sector with wavelets produces more reliable results than the other techniques.

Non-Equilibrium Scaling Applied to the Wake Evolution of a Model Scale Wind Turbine

The present paper addresses the evolution of turbulence characteristics in wind turbine wakes immersed in a turbulent boundary layer. The study thereby focuses on finding physically consistent scaling laws for the wake width, the velocity deficit, and the Reynolds stresses in the far wake region. For this purpose, the concept of an added wake is derived which allows to analyse the self-similarity of the added flow quantities and the applicability of the non-equilibrium dissipation theory. The investigation is based on wind tunnel measurements in the wake of a three-bladed horizontal axis wind turbine model (HAWT) immersed in two neutrally-stratified turbulent boundary layers of different aerodynamic roughness length. The dataset also includes wake measurements for various yaw angles. A high degree of self-similarity is found in the lateral profiles of the velocity deficit and of the added Reynolds stress components. It is shown that these can be described by combined Gaussian shape functions. In the vertical, self-similarity can just be shown in the upper part of the wake. Moreover, it is observed that the degree of self-similarity is affected by the ground roughness. Results suggest an approximately constant anisotropy of the added turbulent stresses in the far wake, and the axial scaling of the added Reynolds stress components is found to be in accordance with non-equilibrium dissipation theory. It predicts a x − 1 decay of the added turbulent intensity I + , and a x − 2 evolution of the added Reynolds shear stresses Δ u i ′ u j ′ ¯ and the velocity deficit Δ u . Based on these findingsa semi-empirical model is proposed for predicting the Reynolds stresses in the far wake region which can easily be coupled with existing analytical wake models. The proposed model is found to be in good agreement with the measurement results.

Digital image recognition based on Fractional-order-PCA-SVM coupling algorithm

This paper use the fractional differential mask operator to describe and deal with the highly self similar digital medical image, then extracting the main features of the these images with principal component analysis, and recognizing these images with SVM algorithm. To provide an efficient algorithm for medical image processing with high degree of self similarity. Based on the analysis of fractional differential theory, principal component analysis theory and support vector machine (SVM) algorithm, designing a Fractional-order-PCA-SVM coupling algorithm for digital image recognition. and taking the digital image in ORL face database as the empirical object, using PCA-NN coupling algorithm, SVM algorithm, PCA-SVM algorithm, Sobel-PCA-NN algorithm, coupling coupling Sobel-SVM coupling algorithm, Sobel-PCA-SVM algorithm, Fractional-order-PCA-NN algorithm and coupling coupling Fractional-order-SVM coupling algorithm as the comparison algorithm, creating four experiments with the same sample, regarding the speed of operation and the accuracy of recognition as the criteria of high evaluation, verifying the superiority of the Fractional-order-PCA-SVM coupling algorithm designed in this paper. The average run time of the Fractional-order-PCA-SVM coupling algorithm only 4.152 s in the four experiments, although the speed of operation is not the highest, it also meets the demand of identification. The reason that the operation speed of the algorithm slightly lower than the three control algorithms is that the three algorithms are monotonic; The average accuracy rate of Fractional-order-PCA-SVM coupling algorithm is 99.2425% in four experiments, significantly better than the eight comparison algorithm; For digital medical image recognition the coupling algorithm designed in this paper is effective.

Non-Local Regularized Variational Model for Image Deblurring under Mixed Gaussian-Impulse Noise

The presence of mixed Gaussian-impulse noise makes image deblurring much more challenging in real-world applications. To guarantee high-quality deblurred images, a non-local regularized variational model is proposed in this paper for restoration of blurred images corrupted by mixed Gaussian-impulse noise. The proposed image restoration model is mainly composed of a mixed L^(1,2) data-fidelity term and a non-local total variation regularizer. The nonlocal regularizer is capable of suppressing staircase-like artifacts and preserving fine structures since it takes full advantage of high degree of self-similarity and redundancy within images. However, the mixed data-fidelity term and non-local regularizer make the image deblurring problem arithmetically difficult to solve. To guarantee solution stability and efficiency, an alternating minimization algorithm is developed to solve the resulting optimization problem. In particular, the original optimization problem can be decomposed into two simpler subproblems. Each subproblem is then efficiently solved using existing numerical method. Comprehensive experiments on grayscale and color test images have been carried out to compare our proposed method with several state-of the- art image restoration methods. Experimental results demonstrated that our proposed method was more capable of removing both blurring effect and mixed Gaussian-impulse noise, as well as preserving fine image details.

Research on a visual weld detection method based on invariant moment features

Purpose– This paper aims to develop a feasible visual weld detection method to solve the problems in multi-layer welding detection (e.g. cover pass welding detection) for seam tracking and non-destructive testing. It seeks for an adaptive and accurate way to determine the edge between the seam and the base metal in the grayscale image of weld automatically. This paper tries to contribute to next-generation real-time robotic welding systems for multi-layer welding.Design/methodology/approach– This paper opted for invariant moments to characterize the seam and the base metal for classification purposes. The properties of invariant moments, such as high degree of self-similarity and separation, affine invariance and repetition invariance, were discussed to verify the adaptability of the invariant moment in weld detection. Then, a weld detection method based on invariant moments was proposed to extract the edge between the seam and the base metal, including image division, invariant moment features extraction, K-Means adaptive thresholding, maximum connected domain detection and edge position extraction.Findings– This paper highlights the significance of high degree of self-similarity and separation, affine invariance and repetition invariance of the invariant moment for weld detection. An adaptive, effective and accurate method is proposed to detect the edge between the seam and the base metal based on invariant moments.Research limitations/implications– It is necessary to verify the applicability of the proposed method in variable welding conditions further. Further works will focus on the establishment of a real-time seam tracking system during the whole multi-layer/multi-pass welding process based on such adaptive visual features.Practical implications– This paper includes the implications for development of an adaptive and real-time weld detection method, which is expected to be applied to online seam tracking in multi-layer welding.Originality/value– This paper presents an accurate weld detection method in multi-layer welding, overcoming difficulties in effectiveness, adaptability and efficiency of existing weld detection methods.

Exploiting the Self-Similarity in ERP Images by Nonlocal Means for Single-Trial Denoising

Event related potentials (ERPs) represent a noninvasive and widely available means to analyze neural correlates of sensory and cognitive processing. Recent developments in neural and cognitive engineering proposed completely new application fields of this well-established measurement technique when using an advanced single-trial processing. We have recently shown that 2-D diffusion filtering methods from image processing can be used for the denoising of ERP single-trials in matrix representations, also called ERP images. In contrast to conventional 1-D transient ERP denoising techniques, the 2-D restoration of ERP images allows for an integration of regularities over multiple stimulations into the denoising process. Advanced anisotropic image restoration methods may require directional information for the ERP denoising process. This is especially true if there is a lack of a priori knowledge about possible traces in ERP images. However due to the use of event related experimental paradigms, ERP images are characterized by a high degree of self-similarity over the individual trials. In this paper, we propose the simple and easy to apply nonlocal means method for ERP image denoising in order to exploit this self-similarity rather than focusing on the edge-based extraction of directional information. Using measured and simulated ERP data, we compare our method to conventional approaches in ERP denoising. It is concluded that the self-similarity in ERP images can be exploited for single-trial ERP denoising by the proposed approach. This method might be promising for a variety of evoked and event-related potential applications, including nonstationary paradigms such as changing exogeneous stimulus characteristics or endogenous states during the experiment. As presented, the proposed approach is for the a posteriori denoising of single-trial sequences.

Use of the theory of fractals in image analysis tasks

A detailed review of the best-known methods for estimating fractal dimension (FD) is given, and their accuracy and computational complexity are demonstrated by estimating the FD of model images with a high degree of self-similarity. A new algorithm for estimating FD by linear transformations is proposed which gives much lower error without significantly increasing the computational complexity.

The second solar spectrum and the hidden magnetism

AbstractApplications of the Hanle effect have revealed the existence of vast amounts of “hidden“ magnetic flux in the solar photosphere, which remains invisible to the Zeeman effect due to cancellations inside each spatial resolution element of the opposite-polarity contributions from this small-scale, tangled field. The Hanle effect is a coherency phenomenon that represents the magnetic modification of the linearly polarized spectrum of the Sun that is formed by coherent scattering processes. This so-called “Second Solar Spectrum” is as richly structured as the ordinary intensity spectrum, but the spectral structures look completely different and have different physical origins. One of the new diagnostic uses of this novel spectrum is to explore the magnetic field in previously inaccessible parameter domains. The earlier view that most of the magnetic flux in the photosphere is in the form of intermittent kG flux tubes with tiny filling factors has thereby been shattered. The whole photospheric volume instead appears to be seething with intermediately strong fields, of order 100G, of significance for the overall energy balance of the solar atmosphere. According to the new paradigm the field behaves like a fractal with a high degree of self-similarity between the different scales. The magnetic structuring is expected to continue down to the 10m scale, 4 orders of magnitude below the current spatial resolution limit.

Modeling and specifications of dynamic agents in fractal manufacturing systems

In order to respond to a rapidly changing manufacturing environment and market, manufacturing systems must be flexible, adaptable, and reusable. The fractal manufacturing system (FrMS) is one of the new manufacturing paradigms that address the need for these characteristics. The FrMS is comprised of a number of “basic components”, each of which consists of five functional modules: (1) an observer, (2) an analyzer, (3) an organizer, (4) a resolver, and (5) a reporter. Each of these modules, using agent technology, autonomously cooperates and negotiates with others while processing its own jobs. The resulting architecture has a high degree of self-similarity, one of the main characteristics of a fractal. Despite the many conceptual advantages of the FrMS, it has not been successfully elaborated and implemented to date because of the difficulties involved in doing so. In this paper, the static functions and dynamic activities of each agent are modeled using the unified modeling language (UML). Then, relationships among agents, working mechanisms of each agent, and several fractal-specific characteristics (self-similarity, self-organization, and goal-orientation) are modeled using the UML. Then, a method for dealing with several types of information such as products, orders, and resources in the FrMS is presented. Finally, a preliminary prototype for the FrMS using Aglets™ is presented.

Indicator dilution methods for measuring blood flow, volume, and other properties of biological systems: a brief history and memoir.

In 1824 Hering introduced an indicator-dilution method for measuring blood velocity. Not until 1897 was the method extended by Stewart to measure blood (volume) flow. For more than two decades, beginning in 1928, Hamilton and colleagues measured blood flow, including cardiac output. They proposed that the first-passsage indicator concentration-time curve could be recovered from observed curves that included recirculation by semilogarithmic extrapolation of the early downslope. Others followed with attempts to fit the complete first-passage curve by various forms, such as by the sum of three exponential terms (three well-stirred compartments in series). Stephenson (1948) thought of looking at indicator-dilution curves as convolutions of indicator input with a probability density function of traversal times through the system. Meier and I reached a similar conclusion, and extended it. The fundamental notion is that there exists a probability density function of transit times, h(t), through the system. We proved that mean transit time t= V/F, where V is volume in which the indicator is distributed. Thus, V, F, and t might all be calculated, or r alone might suffice if one wanted only to know relative blood flow. I extended the analysis to include residue detection of indicator remaining in the system, so that V, F, and could be calculated by external monitoring. Chinard demonstrated the value of simultaneous multiple indicator-dilution curves with various volumes of distribution. Goresky extended the technique to study cell uptake and metabolism. He also found a transform of indicator-dilution output curves (equivalent to multiplying the ordinate by tand dividing the time by t) which made congruent the family of unalike curves obtained by simultaneous injection of indicators with different volumes of distribution. Bassingthwaighte showed the same congruency with the transform of outputs of a single indicator introduced into a system with experimentally varied blood flows. We showed the same congruency for the pulmonary circulation, adding a correction for delays. Success of these transforms suggests that the architecture of the vascular network is a major determinant of the shape of density functions of transit times through the system, and that there is in this architecture, a high degree of self-similarity, implying that the fractal power function is a component in shaping the observed density of transit times. I proposed that the distribution of capillary critical opening pressures, which describes recruitment of vascular paths, may be important in shaping indicator-dilution curves, and that h(t) may be derived from flow-pressure and volume-pressure curves under some circumstances.

Reference architecture for holonic manufacturing systems: PROSA

Future manufacturing systems need to cope with frequent changes and disturbances. As such, their control requires constant adaptation and high flexibility. Holonic manufacturing is a highly distributed control paradigm that promises to handle these problems successfully. It is based on the concept of autonomous co-operating agents, called `holons'. This paper gives an overview of the holonic reference architecture for manufacturing systems as developed at PMA-KULeuven. This architecture, called PROSA, consists of three types of basic holons: order holons, product holons, and resource holons. They are structured using the object-oriented concepts of aggregation and specialisation. Staff holons can be added to assist the basic holons with expert knowledge. The resulting architecture has a high degree of self-similarity, which reduces the complexity to integrate new components and enables easy reconfiguration of the system. PROSA shows to cover aspects of both hierarchical as well as heterarchical control approaches. As such, it can be regarded as a generalisation of the two former approaches. More importantly, PROSA introduces significant innovations: the system structure is decoupled from the control algorithm, logistical aspects can be decoupled from technical ones, and PROSA opens opportunities to achieve more advanced hybrid control algorithms.