The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper, we present an adaptive denoising algorithm based on filter and sparse representation. It employs Wiener filter and Gaussian filter to extract high-frequency components on the noisy image, and simultaneously reduce the influence of noise for clustering. Image is denoised by solving a double-headed ℓ1-optimization problem with the regularization involving dictionary learning and structural...
Image in the collection, transmission and other processes, often affected to some extent, resulting in noise. The purpose of image denoising is obtained from the degraded image noise removal, restore the original image. Traditional denoising methods can filter noise, but at the same time they make the image details fuzzy. The support vector machine based method for image denoising is a good method,thus...
Combining the wavelet analysis and partial differential equations denoising method and applying synthesis algorithm applied to CT image denoising in this paper. It has some advantages: small amount of calculation, pay attention to subtle changes when denoise, highlight the local character of image, overcome the inadequacy of single wavelet transform method to a certain extent. Both theoretical analysis...
Total variation minimization (TVM) is an isotropic functional that penalizes the abrupt changes in all directions in the images. Isotropic TVM is frequently used as image prior as there are typically no dominant direction of the objects in the images. However, in some cases, objects in the image may have a dominant direction. In these cases directional TVM can be preferred instead of isotropic TVM...
A novel enhanced switching median filter incorporating an effective impulse noise detection method called neighborhood based layer discriminative noise detection (NBLDND) is proposed in this paper for de-noising extremely corrupted images. The neighborhood of a pixel is classified into pepper corrupted lower class (PCLC), uncorrupted middle class(UCMC) and salt corrupted higher class (SCHC). The center...
A novel adaptive spatial and transform based approach by fusing Bilateral Filter (BF), Joint Bilateral Filter (JBF) and Wavelet Thresholding (WT) is proposed for image restoration. The Bilateral and Joint Bilateral filter can perform as an edge-preserving smoothing operator and have better behavior near the edges. In the first stage, the noisy image is passed through bilateral filter and some amount...
Sparsity has been shown to be very useful in blind source separation. However, in most cases the sources of interest are not sparse in their current domain and are traditionally sparsified using a predefined transform or a learned dictionary. In this paper, we address the case where the underlying sparse domains of the sources are not available and propose a solution via fusing the dictionary learning...
Photon limitations arise in spectral imaging, nuclear medicine, astronomy and night vision. The Poisson distribution used to model this noise has variance equal to its mean so blind application of standard noise removals methods yields significant artifacts. Recently, overcomplete dictionaries combined with sparse learning techniques have become extremely popular in image reconstruction. The aim of...
The choice of threshold in wavelet based image denoising is very critical. The universal threshold is a global threshold utilized for denoising the wavelet coefficients. An effective approach for the estimation of universal threshold based on spatial context modeling of the wavelet coefficients has been proposed. Spatial contextmodeling involves determination of the correlated pixels within a local...
Removing noise from a digital image is a challenging problem. Application of Gaussian Scale Mixtures (GSM) in the wavelet domain has been reported to be one of the most effective denoising algorithms, published to date. The performance of this algorithm depends on the chosen wavelet representation. In this paper, we introduce an improved wavelet pyramid representation based on the Battle-Lemarie wavelet...
The idea of this paper is to model image denoising using an approach based on partial differential equations (PDE), which describes two dimensional heat diffusion. The two dimensional image function is taken to be the harmonic, when it can be obtained as the solution to the equation describing the the heat diffusion. To achieve this, image denoising is formulated as an optimization problem, in which...
In this paper the problem of image denoising is approached using sparse approximation of local image patches. The small patches are extracted by sliding square windows. An adaptive window selection procedure for local sparse approximation is proposed, which affects the global recovery of underlying image. Ideally the adaptive window selection yields the minimum mean square error (MMSE) in recovered...
In this paper, we proposed a novel image denoising method based on clustering using SURE-LET. This method divides the images into several clusters and minimize the Steins unbiased risk estimator (SURE) of each cluster independently, which makes different clusters of pixels were denoised by different threshold functions in the image domain. The proposed method included the traditional SURE-LET as its...
The problem of reconstructing digital images from degraded measurement is regarded as a problem of importance in various fields of engineering and imaging sciences. The main goal of denoising is to restore a noisy image to produce visually high quality image. In general, image denoising imposes a compromise between noise reduction and preserving significant image details. In this paper we propose...
In this paper, the problem of balancing the noise removing and the image details preserving is considered. To remove noise adaptively, local dictionaries and sparse coding techniques are used. For a noised image patch, the local dictionary corresponding to it and the sparse coding technique are used to generate the sparse coding vector of the given patch. Then the noise of the given patch can be removed...
The core of image denoising is making a trade-off between removing noise and preserving details of noised image. To remove noise, the denoising algorithm based on K-SVD is employed in this paper. Though the power of such denoising algorithm has been verified by a mount of experiments, many meaningful details of noised image cannot be well maintained. To preserve details of noised image, therefore,...
This paper attempts to undertake the study of two types of noise such as Salt and Pepper (SPN), Speckle (SPKN). Different noise densities have been removed by using four types of filters as meidan filter, Lee filter, Kuan filter, Frost filter, and Wavelet based Bivariate Shrinkage function. Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due...
A 2D signal is often used as an image signal in the application of a digital image, which could produce noise in the processing of image acquisition. It is too rough of a traditional method to be used for noise suppression; regarding noise suppression, it will also lose part of the original signal so that makes the image blurred. This paper introduces image reduction based on the wavelet analysis...
The presence of noise interference signal may cause problems in signal and image analysis; hence signal and image de-noising is often used as a preprocessing stage in many signal processing applications. In this paper, a new method is presented for image de-noising based on fourth order partial differential equations (PDEs) and wavelet transform. In the existing wavelet thresholding methods, the final...
We propose a low-rank subspace recovery and image denoising method for face recognition. Traditional subspace methods commonly assume that face images from a single class lie on a low-rank subspace. However, due to shadows, specularities, occlusion and corruption, real face images seldom reveal such low-rank structure. To address this problem, we cast the problem of recovering face subspace from noisy...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.