2,119 research outputs found

    A Spatially Adaptive Edge-Preserving Denoising Method Based on Fractional-Order Variational PDEs

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    Image denoising is a basic problem in image processing. An important task of image denoising is to preserve the significant geometric features such as edges and textures while filtering out noise. So far, this is still a problem to be further studied. In this paper, we firstly introduce an edge detection function based on the Gaussian filtering operator and then analyze the filtering characteristic of the fractional derivative operator. On the basis, we establish the spatially adaptive fractional edge-preserving denoising model in the variational framework, discuss the existence and uniqueness of our proposed model solution and derive the nonlinear fractional Euler-Lagrange equation for solving our proposed model. This forms a fractional order extension of the first and second order variational approaches. Finally, we apply the proposed method to the synthetic images and real seismic data denoising to verify the effectiveness of our method and compare the experimental results of our method with the related state-of-the-art methods. Experimental results illustrate that our proposed method can not only improve the signal to noise ratio (SNR) but also adaptively preserve the structural information of an image compared with other contrastive methods. Our proposed method can also be applied to remote sensing imaging, medical imaging and so onThe work of Dehua Wang was supported in part by the Science and Technology Planning Project of Shaanxi Province under Grant 2020JM-561, in part by the Postdoctoral Foundation of China under Grant 2019M663462, in part by the Innovative Talents Cultivate Program of Shaanxi Province under Grant 2019KJXX-032, in part by the President Fund of Xiā€™an Technological University under Grant XAGDXJJ17026, and in part by the Teaching Reform Project of Xiā€™an Technological University under Grant 18JGY08. The work of Juan J. Nieto was supported in part by the Agencia Estatal de Investigacion (AEI) of Spain under Grant MTM2016-75140-P, and in part by the European Community Fund FEDER. The work of Xiaoping Li was supported in part by the NSFC under Grant 61701086, and in part by the Fundamental Research Funds for the Central Universities under Grant ZYGX2016KYQD143S

    Denoising techniques - a comparison

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    Visual information transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmission is often corrupted with noise. The received image needs processing before it can be used in applications. Image denoising involves the manipulation of the image data to produce a visually high quality image. This thesis reviews the existing denoising algorithms, such as filtering approach, wavelet based approach, and multifractal approach, and performs their comparative study. Different noise models including additive and multiplicative types are used. They include Gaussian noise, salt and pepper noise, speckle noise and Brownian noise. Selection of the denoising algorithm is application dependent. Hence, it is necessary to have knowledge about the noise present in the image so as to select the appropriate denoising algorithm. The filtering approach has been proved to be the best when the image is corrupted with salt and pepper noise. The wavelet based approach finds applications in denoising images corrupted with Gaussian noise. In the case where the noise characteristics are complex, the multifractal approach can be used. A quantitative measure of comparison is provided by the signal to noise ratio of the image

    Bayesian Segmentation in Signal with Multiplicative Noise Using Reversible Jump MCMC

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    This paper proposes the important issues in signal segmentation. The signal is disturbed by multiplicative noise where the number of segments is unknown. A Bayesian approach is proposed to estimate the parameter. The parameter includes the number of segments, the location of the segment, and the amplitude. The posterior distribution for the parameter does not have a simple equation so that the Bayes estimator is not easily determined. Reversible Jump Markov chain Monte Carlo (MCMC) method is adopted to overcome the problem. The Reversible Jump MCMC method creates a Markov chain whose distribution is close to the posterior distribution. The performance of the algorithm is shown by simulation data. The result of this simulation shows that the algorithm works well. As an application, the algorithm is used to segment a Synthetic Aperture Radar (SAR) signal. The advantage of this method is that the number of segments, the position of the segment change, and the amplitude are estimated simultaneously

    A Novel Fractional-Order Variational Approach for Image Restoration Based on Fuzzy Membership Degrees

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    We propose a new fractional-order (space and time) total variation regularized model for multiplicative noise removal in this research article. We use the regularly varying fuzzy membership degrees to characterize the likelihood of a pixel related to edges, texture regions, and flat regions to improve model efficiency. This approach is capable of maintaining edges, textures, and other image information while significantly reducing the blocky effect. We opt for the option of local actions. In order to efficiently find the minimizer of the prescribed energy function, the semi-implicit gradient descent approach is used (which derives the corresponding fractional-order Euler-Lagrange equations). The existence and uniqueness of a solution to the suggested variational model are proved. Experimental results show the efficiency of the suggested model in visual enhancement, preserving details and reducing the blocky effect while extracting noise as well as an increase in the PSNR (dB), SSIM, relative error, and less CPU time(s) comparing to other schemes

    Integrated approach to cosmology: Combining CMB, large-scale structure and weak lensing

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    Recent observational progress has led to the establishment of the standard Ī›\LambdaCDM model for cosmology. This development is based on different cosmological probes that are usually combined through their likelihoods at the latest stage in the analysis. We implement here an integrated scheme for cosmological probes, which are combined in a common framework starting at the map level. This treatment is necessary as the probes are generally derived from overlapping maps and are thus not independent. It also allows for a thorough test of the cosmological model and of systematics through the consistency of different physical tracers. As a first application, we combine current measurements of the Cosmic Microwave Background (CMB) from the Planck satellite, and galaxy clustering and weak lensing from SDSS. We consider the spherical harmonic power spectra of these probes including all six auto- and cross-correlations along with the associated full Gaussian covariance matrix. This provides an integrated treatment of different analyses usually performed separately including CMB anisotropies, cosmic shear, galaxy clustering, galaxy-galaxy lensing and the Integrated Sachs-Wolfe (ISW) effect with galaxy and shear tracers. We derive constraints on Ī›\LambdaCDM parameters that are compatible with existing constraints and highlight tensions between data sets, which become apparent in this integrated treatment. We discuss how this approach provides a complete and powerful integrated framework for probe combination and how it can be extended to include other tracers in the context of current and future wide field cosmological surveys.Comment: 29 pages, 19 figures, 3 tables, to appear in PRD, updated following referee's comments including small changes in result

    Drift Correction Methods for gas Chemical Sensors in Artificial Olfaction Systems: Techniques and Challenges

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    In this chapter the authors introduce the main challenges faced when developing drift correction techniques and will propose a deep overview of state-of-the-art methodologies that have been proposed in the scientific literature trying to underlying pros and cons of these techniques and focusing on challenges still open and waiting for solution
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