Enajenación del trabajo en los Manuscritos parisinos

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Efficient generators in simulated annealing

Efficient generators in simulated annealing

Let F be the original gray level image and G be the observed image degraded by additive independent identically distributed Gaussian noise of zero mean and standard deviation a.. Model b[r]

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Nonlocal means scheme for image noise 
		reduction

Nonlocal means scheme for image noise reduction

The nonlocal means (NLM) filtering scheme has gained an increasingly interest in the last decade for its great performance in image restoration. This scheme yields attractive results in removing Gaussian noise from an image by replacing the intensity value of each pixel by weighted average of the pixel intensities in a search neighborhood in the image. It is primarily based on repeated patterns that often exit in images. The selection of the kernel functions in such nonlocal means image restoration scheme is a major concern of researchers in order to improve the restored image quality. The Gaussian function is a standard kernel function commonly applied in the NLM filtering. In this paper, two functions for a nonlocal means image filtering scheme are proposed through a specific NLM method using an adaptive window size that varies according to the characteristics of the search regions in the image. Zero-mean Gaussian noise with different values of standard deviation corrupting various images of different characteristics has been used in the computer simulations. Mean squared error (MSE) and mean absolute error (MAE) have been used as measuring indices for the quality of the output restored image. Results show that these two functions work well and yield better performance mainly for images with a lot of details and edges than the conventional NLM scheme that uses the Gaussian kernel function and a fixed window size.
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Models of visual feature detection and spike coding in the nervous system

Models of visual feature detection and spike coding in the nervous system

Figure 3.14: Left: Result of adding zero mean, Gaussian white noise n with standard deviation $ before passing the scan lines of the image through a five level uniform quantizer, illustr[r]

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A Hybrid Filtering Technique for Eliminating Gaussian Noise and Impulse Noise on Digital Images

A Hybrid Filtering Technique for Eliminating Gaussian Noise and Impulse Noise on Digital Images

noise of zero mean with σ=200 shows superior performance for testing under various noise levels. In order to get effective filtering performance, already existing hybrid filters are trained with image data and tested using equal noise density. But in practical situation, information about the noise density of the received signal is unpredictable one. Therefore; in this paper, the ANFIS architecture is trained using denoised three well known images which are corrupted by adding different noise density levels. Noise density with 0.1 of impulse noise and gaussian noise of zero mean and σ=200 gave optimum solution for both lower and higher level noise corruption. Therefore images are corrupted with 45% of noise is selected for training. Then the performance error of the given trained data and trained network structure are observed for each network. Among these network structures, the trained network structure with the minimum error level is selected (10 -3 ) and this trained network structures are fixed for testing the received image signal. Also, to ensure faster processing, only the corrupted pixels from test images are identified and processed by the optimized neural network structure. As uncorrupted pixels do not require further processing, they are directly taken as the output.
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A Significant Methods for Image Enhancement

A Significant Methods for Image Enhancement

In this, denoising plays a vital role. This is due to the emergence of noisy image sequences, such as those captured by cellular phones, recorded by webcams and old archive movies. In many cases, the noise is assumed to be zero mean Gaussian noise, is also common in the still image denoising literature.In the proposed method ,a qualitative image ehnacement approaches were used for improving the quality of the image.Figure 1 shows the block diagram of the enchancement technique carried in the proposed method for bio medical images.
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Dynamic Wavelet Thresholding based Image Restoration

Dynamic Wavelet Thresholding based Image Restoration

A signal or an image can be unfortunately corrupted by various factors which effects as noise during acquisition or transmission. The de-noising process is described as to remove the noise while retaining and not affecting the quality of processed signal or image. The conventional way of de-noising is to remove the noise from a signal or an image is to use a low or band pass filter with cut off frequencies. However the filtering techniques are able to remove only a relevant of the noise, they are incapable if the noise in the band of the signal is to be analyzed. Therefore, many de-noising techniques are proposed to overcome this problem. One of those is the wavelet transform (WT) processes.
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Title: A Review Paper on Noise Removal in Grey Scale Images

Title: A Review Paper on Noise Removal in Grey Scale Images

(Ansari & Mangla, 2015) had proposed a method for Eliminating Noise from Mixed Noisy Image by using Modified Bilateral Filter. To remove noise from images neighborhood processing is to be used. In Neighborhood processing, a function is applied to a neighborhood of each pixel. During this a rectangular mask (usually with sides of odd length) is moved over the given image. As we do this, we create a new image whose pixels have grey values calculated from the grey values under the mask. Mixed noise is considered as mixture of Gaussian noise and impulse noise. Some of the techniques are implemented in the work and analyzed in terms of various performance matrices. By understanding advantages, drawbacks and limitation of these techniques, we proposed an optimum technique for removing mixed noise from color image. Author proposed a SBF technique for this purpose.
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Restoration of Color Image Corrupted by Gaussian Noise

Restoration of Color Image Corrupted by Gaussian Noise

Image noise is the random variation of brightness or color information in images produced by the sensor and circuitry of a scanner or digital camera. The development of techniques for noise removal is of paramount importance for image-based measurement systems [1]. In-order to smooth out from noise many filtering architectures have been proposed in the literature [2]. The goal of the filtering action is to cancel noise while preserving the integrity of edge and detail information, non linear approaches [3] generally provide more satisfactory results than linear techniques. However, a common drawback of the practical use of these methods is the fact that they usually require some “a priori” knowledge about the amount of noise corruption. Unfortunately such information is not available in real time applications.
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Optimal Input Excitation Design for Nonparametric Uncertainty Quantification of Multi Input Multi Output Systems

Optimal Input Excitation Design for Nonparametric Uncertainty Quantification of Multi Input Multi Output Systems

Consequently, the orthogonal approach is preferred for parameterized modeling. 3) The FRFs associated with the shaker input are severely distorted at frequencies higher than 100 Hz. Unlike the classical function in Fig. 3(a) and its coherence in Fig. 3(b) which indicate unreliable modelling quality at these frequencies, the source of distortions in the robust MIMO LPM method of (Pintelon, Vandersteen, et al. 2011) are associated with noise/nonlinear contributions. Physically, the nonlinear distortion is due to the nature of the connection between the shaker and the beam, i.e., the rubber- band (see Fig. 1). The reason for using a rubber-band instead of a direct connection (adhesive wax, screws, etc.) is to match the impedance of the (control) input signals realized by the piezo-patches with the (disturbance) signal generated by the electromagnetic shaker (Pintelon et al. 2012). 4) In the results of the orthogonal multisine method, the contribution of nonlinear distortions can be neglected since the total variance is 40 dB below the BLA. However, depending on the application, this may become non-negligible. Before proceeding to the assessment of input excitation optimization in MIMO smart structures, two remarks should be made regarding the importance of the MIMO robust LPM: a) The total variance not only reflects the quality of the estimated
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Survey on Noise Removal in MRI Brain Image for Various Filters

Survey on Noise Removal in MRI Brain Image for Various Filters

Wiener2 is a 2-D adaptive noise removal filter. The wiwner2 function applies a wiener filter which is a type of linear filter to an image adaptively, tailoring itself to local image variance. Where the variance is large, wiener2 performs little smoothing. Where the variance is small, wiener2 performs more smoothing. This approach often produces better result than linear filtering. The adaptive filter is more selective than a comparable linear filter, preserving edges and other high frequency parts of an image. In addition, there are no design tasks; the wiener2 function handles all preliminary computations, and implements the filter for preliminary computations, and implements the filter for an input image. Best suitable to remove Gaussian noise.
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Title: A Comparative Review Paper for Noise Models and Image Restoration Techniques

Title: A Comparative Review Paper for Noise Models and Image Restoration Techniques

2. NOISE MODEL:-Noise is the unwanted element produced in the image. During image acquisition or transmission, several factors are responsible for generating noise in the image [3]. Depending on the type of disturbance, the noise can affect the image to a different extent. Generally researchers aim to remove certain kind of noise. Therefore researchers identify certain kind of noise and apply different algorithms accordingly to eliminate the noise. Image noise can be categorised as Impulse noise (Salt-and-pepper noise), Amplifier noise (Gaussian noise), Shot noise, Multiplicative noise (Speckle noise), Quantization noise (uniform noise), and Periodic noise [3].
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Feature Extraction of Brain Tumor Using MRI

Feature Extraction of Brain Tumor Using MRI

The median is a more robust average than the mean and so a single very unrepresentative pixel in a neighbourhood will not affect the median value significantly. Since the median value must actually be the value of one of the pixels in the neighbourhood, the median filter does not create new unrealistic pixel values when the filter straddles an edge. For this reason the median filter is much better at preserving sharp edges than the mean filter.[5]

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An Algorithm of Suppressing Image Noise Based on Wavelet Threshold Function and Improved Median Filtering

An Algorithm of Suppressing Image Noise Based on Wavelet Threshold Function and Improved Median Filtering

estimated. To ensure a good precondition for the subsequent image processing noise. For Gauss noise, new wavelet threshold function with the idea of limitation is applied, and the appropriate control variables m and  are proposed to decrease the deviation between the wavelet coefficients and the original wavelet coefficients. The experiment results show that the noise mixed by both of the salt-and-pepper noise and the Gaussian noise is effectively suppressed. The image information is better preserved.
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IMAGE DUPLICATION AND ROTATION DETECTION METHODS FOR STORAGE UTILIZATION

IMAGE DUPLICATION AND ROTATION DETECTION METHODS FOR STORAGE UTILIZATION

Deconvolution is the process used to reverse the effects of convolution on image.The concept of deconvolution is popular used in the techniques of signal processing and image processing.It is the process of separation of blurred image from the original image. The objective is to find the original image from the observed blurred image which is degraded by an additive Gaussian noise and blur kernel.The noise from the given input image is found by using Fourier domain deconvolution.We can retain the spatial information of the image using Fourier domain.To remove the artifacts from the image without excessive slow down in computation,iterated shrinkage deconvolution technique used.With these results will be improved in both visual quality and mean square error. In fact, the wiener filter method is used for deconvolution.Its purpose to decrease the amount of blur available in the image by comparison with an estimation of the desired noiseless image.This technique gives proper solution for multiple images by removing artifacts without excessive slow down.This algorithm can computationally be improved by providing very low complexity. To apply the MATLAB edge taper function is to smooth the transition between the opposite sides of the images(part of the Image Processing Toolbox).
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Improved Non-Local Means Algorithm Based on Dimensionality Reduction

Improved Non-Local Means Algorithm Based on Dimensionality Reduction

The Non-Local Means algorithm searches neighboring patches to match with the reference patch. The original algorithm requires an extensive amount of time to select patches similar to the reference patch. These similar patches contribute to the weighted averaging process to denoise the center pixel of the reference patch. The computation time for NLM algorithm can be reduced by improving this searching process. In our method, we have created feature vectors for the noisy image. Then we have implemented a statistical t-test on these feature vectors and reduced their dimensionality. These reduced feature vectors contribute to the denoising process. Our proposed method reduces the computational time and improves the overall performance of the original NLM algorithm.
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Performance of Fuzzy Filter and Mean Filter for Removing Gaussian Noise

Performance of Fuzzy Filter and Mean Filter for Removing Gaussian Noise

In this section, experimental results are presented which explored the characteristics of the various filters used and tested. The comparative analysis has been presented on the basis of different standard deviation of noise for the original image (512*512) which is shown in the Table (1). The result is taken by comparing the performance of Fuzzy Filter and Mean Filter on the basis of PSNR and MSE value.

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MATLAB Techniques for Enhancement of Liver DICOM Images

MATLAB Techniques for Enhancement of Liver DICOM Images

Abstract – DICOM has become a standard for medical imaging. Its purpose is to standardize digital medical imaging and data for easy access and sharing. There are many commercial viewers that support DICOM image format and can read metadata, but image displaying is not always optimal. One of the most common degradations in medical images is their poor contrast quality and noise. The DICOM image consists of speckle noise. While the image is enhanced, the different types of noises present in the image are also enhanced [5]. During our study we find there is problem in correct displaying for DICOM images in MATLAB. And in this paper we develop new method for correct displaying and de-noising of DICOM images concentrating on liver images.
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International Journal of Computer Science and Mobile Computing

International Journal of Computer Science and Mobile Computing

It is a better method because it extracts the features in an image without disturbing its features. There are certain criteria to improve current methods of edge detection. The first and most obvious is low error rate. It is important that edges occurring in images should not be missed. The second criterion is that the edge points be well localized i.e. the distance between the edge pixels as found by the detector and the actual edge should be minimum. A third criterion is to have only one response to a single edge [4].

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Online Full Text

Online Full Text

results, and have become the most popular method in face recognition. But a real system may encounter with some difficulties which are unexpected, for example when the camera or the subject is moving, when the camera is defocus or when the image has been influenced by noise, we need methods to be robust to such problems.

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Importance of Statistical Measures in Digital Image Processing

Importance of Statistical Measures in Digital Image Processing

Our simulation results show that each statistical measure has significant importance depending upon requirements. So selection of statistical measures is very important and should be done wisely. By using simulated results as reference it is very easy to select the statistical parameter before going for a complex image processing technique. Like from simulation results we concluded that arithmetic mean, geometric mean, harmonic mean all three can be used for removing Gaussian noise but geometric mean preserve the image details better. Contra-harmonic mean filter can be used to remove salt or pepper noise for different values of R. Median is commonly used for salt and pepper noise. Max and min filter is used for filtering pepper and salt noise respectively. Mid-point can be used to reduce Gaussian and uniform noise in images. Alpha-trimmed filter can be used to remove more than once type of noise. Mode is basically used for image classification. Standard deviation based filter can used in radar for pattern recognition. Covariance and variance based filter can be used for edge sharpening, appearance based face detection and edge positioning. Skewness can be used for image surface recognition and kurtosis for image resolution.
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