An Image Filter Based on Shearlet Transformation and Particle Swarm Optimization Algorithm

Digital image is always polluted by noise and made data postprocessing difficult. To remove noise and preserve detail of image as much as possible, this paper proposed image filter algorithm which combined the merits of Shearlet transformation and particle swarm optimization (PSO) algorithm. Firstly, we use classical Shearlet transform to decompose noised image into many subwavelets under multiscale and multiorientation. Secondly, we gave weighted factor to those subwavelets obtained. Then, using classical Shearlet inverse transform, we obtained a composite image which is composed of those weighted subwavelets. After that, we designed fast and rough evaluation method to evaluate noise level of the new image; by using this method as fitness, we adopted PSO to find the optimal weighted factor we added; after lots of iterations, by the optimal factors and Shearlet inverse transform, we got the best denoised image. Experimental results have shown that proposed algorithm eliminates noise effectively and yields good peak signal noise ratio (PSNR)

Improved Wavelet Threshold for Image De-noising

With the development of communication technology and network technology, as well as the rising popularity of digital electronic products, an image has become an important carrier of access to outside information. However, images are vulnerable to noise interference during collection, transmission and storage, thereby decreasing image quality. Therefore, image noise reduction processing is necessary to obtain higher-quality images. For the characteristics of its multi-analysis, relativity removal, low entropy, and flexible bases, the wavelet transform has become a powerful tool in the field of image de-noising. The wavelet transform in application mathematics has a rapid development. De-noising methods based on wavelet transform is proposed and achieved with good results, but shortcomings still remain. Traditional threshold functions have some deficiencies in image de-noising. A hard threshold function is discontinuous, whereas a soft threshold function causes constant deviation. To address these shortcomings, a method for removing image noise is proposed in this paper. First, the method decomposes the noise image to determine the wavelet coefficients. Second, the wavelet coefficient is applied on the high-frequency part of the threshold processing by using the improved threshold function. Finally, the de-noised images are obtained to rebuild the images in accordance with the estimation in the wavelet-based conditions. Experiment results show that this method, discussed in this paper, is better than traditional hard threshold de-noising and soft threshold de-noising methods, in terms of objective effects and subjective visual effects

Effects of Non-Local Diffusion on Structural MRI Preprocessing and Default Network Mapping: Statistical Comparisons with Isotropic/Anisotropic Diffusion

Neuroimaging community usually employs spatial smoothing to denoise magnetic resonance imaging (MRI) data, e.g., Gaussian smoothing kernels. Such an isotropic diffusion (ISD) based smoothing is widely adopted for denoising purpose due to its easy implementation and efficient computation. Beyond these advantages, Gaussian smoothing kernels tend to blur the edges, curvature and texture of images. Researchers have proposed anisotropic diffusion (ASD) and non-local diffusion (NLD) kernels. We recently demonstrated the effect of these new filtering paradigms on preprocessing real degraded MRI images from three individual subjects. Here, to further systematically investigate the effects at a group level, we collected both structural and functional MRI data from 23 participants. We first evaluated the three smoothing strategies' impact on brain extraction, segmentation and registration. Finally, we investigated how they affect subsequent mapping of default network based on resting-state functional MRI (R-fMRI) data. Our findings suggest that NLD-based spatial smoothing maybe more effective and reliable at improving the quality of both MRI data preprocessing and default network mapping. We thus recommend NLD may become a promising method of smoothing structural MRI images of R-fMRI pipeline

Machine learning for advancing low-temperature plasma modeling and simulation

Machine learning has had an enormous impact in many scientific disciplines. Also in the field of low-temperature plasma modeling and simulation it has attracted significant interest within the past years. Whereas its application should be carefully assessed in general, many aspects of plasma modeling and simulation have benefited substantially from recent developments within the field of machine learning and data-driven modeling. In this survey, we approach two main objectives: (a) We review the state-of-the-art focusing on approaches to low-temperature plasma modeling and simulation. By dividing our survey into plasma physics, plasma chemistry, plasma-surface interactions, and plasma process control, we aim to extensively discuss relevant examples from literature. (b) We provide a perspective of potential advances to plasma science and technology. We specifically elaborate on advances possibly enabled by adaptation from other scientific disciplines. We argue that not only the known unknowns, but also unknown unknowns may be discovered due to the inherent propensity of data-driven methods to spotlight hidden patterns in data

Polynomial fitting and total variation based techniques on 1-D and 2-D signal denoising

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2010.Thesis (Master's) -- Bilkent University, 2010.Includes bibliographical references leaves 167-176.New techniques are developed for signal denoising and texture recovery. Geometrical theory of total variation (TV) is explored, and an algorithm that uses quadratic programming is introduced for total variation reduction. To minimize the staircase effect associated with commonly used total variation based techniques, robust algorithms are proposed for accurate localization of transition boundaries. For this boundary detection problem, three techniques are proposed. In the first method, the 1−D total variation is applied in first derivative domain. This technique is based on the fact that total variation forms piecewise constant parts and the constant parts in the derivative domain corresponds to lines in time domain. The boundaries of these constant parts are used as the transition boundaries for the line fitting. In the second technique proposed for boundary detection, a wavelet based technique is proposed. Since the mother wavelet can be used to detect local abrupt changes, the Haar wavelet function is used for the purpose of boundary detection. Convolution of a signal or its derivative family with this Haar mother wavelet gives responses at the edge locations, attaining local maxima. A basic local maximization technique is used to find the boundary locations. The last technique proposed for boundary detection is the well known Particle Swarm Optimization (PSO). The locations of the boundaries are randomly perturbed yielding an error for each set of boundaries. Pursuing the personal and global best positions, the boundary locations converge to a set of boundaries. In all of the techniques, polynomial fitting is applied to the part of the signal between the edges. A more complicated scenario for 1−D signal denoising is texture recovery. In the technique proposed in this thesis, the periodicity of the texture is exploited. Periodic and non-periodic parts are distinguished by examining total variation of the autocorrelation of the signal. In the periodic parts, the period size was found by PSO evolution. All the periods were averaged to remove the noise, and the final signal was synthesized. For the purpose of image denoising, optimum one dimensional total variation minimization is carried to two dimensions by Radon transform and slicing method. In the proposed techniques, the stopping criterion for the procedures is chosen as the error norm. The processes are stopped when the residual norm is comparable to noise standard deviation. 1−D and 2−D noise statistics estimation methods based on Maximum Likelihood Estimation (MLE) are presented. The proposed denoising techniques are compared with principal curve projection technique, total variation by Rudin et al, total variation by Willsky et al, and curvelets. The simulations show that our techniques outperform these widely used techniques in the literature.Yıldız, AykutM.S

Image Segmentation Techniques: A Survey

Segmenting an image utilizing diverse strategies is the primary technique of Image Processing. The technique is broadly utilized in clinical image handling, face acknowledgment, walker location, and so on. Various objects in an image can be recognized using image segmentation methods. Researchers have come up with various image segmentation methods for effective analysis. This paper presents a survey and sums up the designs process of essential image segmentation methods broadly utilized with their advantages and weaknesses

Image Segmentation Techniques: A Survey

Segmenting an image utilizing diverse strategies is the primary technique of Image Processing. The technique is broadly utilized in clinical image handling, face acknowledgment, walker location, and so on. Various objects in an image can be recognized using image segmentation methods. Researchers have come up with various image segmentation methods for effective analysis. This paper presents a survey and sums up the designs process of essential image segmentation methods broadly utilized with their advantages and weaknesses

Contributions en optimisation topologique : extension de la méthode adjointe et applications au traitement d'images

De nos jours, l'optimisation topologique a été largement étudiée en optimisation de structure, problème majeur en conception de systèmes mécaniques pour l'industrie et dans les problèmes inverses avec la détection de défauts et d'inclusions. Ce travail se concentre sur les approches de dérivées topologiques et propose une généralisation plus flexible de cette méthode rendant possible l'investigation de nouvelles applications. Dans une première partie, nous étudions des problèmes classiques en traitement d'images (restauration, inpainting), et exposons une formulation commune à ces problèmes. Nous nous concentrons sur la diffusion anisotrope et considérons un nouveau problème : la super-résolution. Notre approche semble meilleure comparée aux autres méthodes. L'utilisation des dérivées topologiques souffre d'inconvénients : elle est limitée à des problèmes simples, nous ne savons pas comment remplir des trous ... Dans une seconde partie, une nouvelle méthode visant à surmonter ces difficultés est présentée. Cette approche, nommée voûte numérique, est une extension de la méthode adjointe. Ce nouvel outil nous permet de considérer de nouveaux champs d'application et de réaliser de nouvelles investigations théoriques dans le domaine des dérivées topologiques.Nowadays, topology optimization has been extensively studied in structural optimization which is a major interest in the design of mechanical systems in the industry and in inverse problems with the detection of defects or inclusions. This work focuses on the topological derivative approach and proposes a more flexible generalization of this method making it possible to address new applications. In a first part, we study classical image processing problems (restoration, inpainting), and give a common framework to theses problems. We focus on anisotropic diffusion and consider a new problem: super-resolution. Our approach seems to be powerful in comparison with other methods. Topological derivative method has some drawbacks: it is limited to simple problems, we do not know how to fill holes, ... In a second part, to overcome these difficulties, an extension of the adjoint method is presented. Named the numerical vault, it allows us to consider new fields of applications and to explore new theoretical investigations in the area of topological derivative