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Stochastic techniques have been widely employed to improve the quality of noisy images. This research paper analyzes the dispersion parameter of the Fokker Plank equation (Kolmogorov Forward equation), which forms the bases to build a transition probability matrix. The empirical results depict that besides being dependent on an image this parameter also depends on the type of noise. Processing time...
Aiming for the denoising problem for infrared image, a novel algorithm is presented based on mixed statistical model in nonsubsampled contourlet domain. The noise coefficients which affect infrared image quality are generally considered to obey Gaussian distribution in nonsubsampled contourlet transform domain. At the same time, the original signal coefficients have the features of sharper peak at...
A method based on sparse denoising autoencoder for denoising hybrid noises in image is proposed in this paper. The method is experimented on natural images and the performance is evaluated in terms of peak signal to noise ratio (PSNR). By specifically designing the training process of sparse denoising autoencoder, our model not only achieves good performance on single kind of noises, but also is relatively...
A new contourlet-based method is introduced for reducing noise in images corrupted by additive white Gaussian noise. This method takes into account the statistical dependencies among the contourlet coefficients of different scales. In view of this, a non-Gaussian multivariate distribution is proposed to capture the across-scale dependencies of the contourlet coefficients. This model is then exploited...
Statistical image modeling has attracted great attention in the field of image denoising. In this work, a new image denoising method in the contourlet domain is introduced in which the contourlet coefficients of images are modeled by using the Bessel k-form prior. A noisy image is decomposed into a low frequency approximation sub-image and a series of high frequency detail sub-images at different...
In this paper, we present a brushlet-based block matching 3D (BM3D) method to collaboratively denoise ultrasound images. Through dividing image into multiple blocks, we group them based on similarity. Then, grouped blocks sharing similarity form a 3D image volume. For each volume, brushlet thresholding is applied to remove noise in the frequency domain. Upon completion of individual filtering, the...
In this paper, a very efficient image denoising scheme, which is called nonlocal means based on bidirectional principal component analysis, is proposed. Unlike conventional principal component analysis (PCA) based methods, which stretch a 2D matrix into a 1D vector and ignores the relations between different rows or columns, we adopt the technique of bidirectional PCA (BDPCA), which preserves the...
In this paper, we propose a new image denoising scheme that is an integration of a content-adaptive guided filter and a collaborative Wiener filter. The proposed scheme consists of two steps. First a content-adaptive guided filter, which smoothes image based on spatial similarity within a local window, is applied. The content-adaptive guided filter can efficiently preserve edges while smoothing noise...
We first briefly recall how to model occlusion and scaling in natural images through the use of a stochastic model, the scaling dead leaves model. Then we give a statistical estimator for its scaling parameters, which are related to the regularity of images. Last we show how this model can be used as an a priori for image denoising, in the framework of wavelet coefficients thresholding.
Wavelet transforms have been utilised effectively for image denoising, providing a means to exploit the relationships between coefficients at multiple scales. In this paper, a modified structure is presented that enables the utilisation of an unlimited number of wavelet filters. An alternative denoising technique is thus proposed with a simple approach for the utilisation of multiple wavelet filters...
Image denoising is the most basic inverse imaging problem. As an under-determined problem, appropriate definition of image priors to regularize the problem is crucial. Among recent proposed priors for image denoising are: i) graph Laplacian regularizer where a given pixel patch is assumed to be smooth in the graph-signal domain; and ii) self-similarity prior where image patches are assumed to recur...
Noise suppression is an integral part of any image processing task Noise significantly degrades the image quality and hence makes it difficult for the observer to discriminate fine detail of the images especially in diagnostic examinations. Through decades of research, mass articles on image denoising have been proposed The effect of noise in the images can be reduced by using either spatial filtering...
Considering the problem that structure information can be easily lost when the edge and texture regions of image are denoised by the Non-Local Means (NL-Means) denoising algorithm, and a NL-Means image denoising algorithm based on edge detection is proposed in this paper. Firstly the edge detection in the noise image is got by using the improved Sobel operator, and then the detecting result is used...
In this work, we employ a pair wise Markov Random Field (MRF) and a Conditional Random Field (CRF) for bi-level image segmentation and denoising. For both tasks, the Ising pair wise model and the Iterative Conditional Mode (ICM) inference method are implemented, assuming the parameters of the unary and pair wise potentials are known. Experimental results demonstrate the effectiveness of the proposed...
In this paper, we propose an improved median filtering algorithm by adding filtration function before replacing the value of the median position with the median and doing multiple processing of median filtering to overcome the shortcoming of the traditional Median Filtering Algorithm. Experimental results are shown that an image after processed by the improved algorithm is hard to find image noise,...
In this paper, we propose an image denoising model by using low-rank dictionary and sparse representation (LRSR). The K-SVD algorithm learns a universal dictionary for all patches in an image and the NLM exploits similarities of nonlocal patches, both achieve effective denoising performance. Motivated by these methods, we propose to use a low-rank dictionary for each cluster of similar patches and...
Speckle noise contaminates medical ultrasound images and the suppression of speckle noise is valuable for image interpretation. This paper presents a new method for speckle suppression by integrating the non-local means (NLM) with the McIlhagga-based anisotropic diffusion (MAD). The MAD is first used to get a diffused image from the initial noisy image, and then the NLM is conducted to get a final...
We propose in this paper a novel example-based method for Gaussian denoising of CT images. In the proposed method, denoising is performed with the help of a set of example CT images. We construct, from the example images, a database consisting of high and low-frequency patch pairs and then use the Markov random field to denoise. The proposed denoising method can restore the high-frequency band that...
Image noise is difficult to avoid during the image acquisition and communication, and thus we need to suppress noise in the low level vision. Among all of the existing image denoising methods, image priors, such as hyper-Laplacian priors of the heavy-tailed distribution of image gradient, play an important role. However, many denoising methods tend to smooth the fine textures while suppressing noise,...
The paper proposes an adaptive shock filter to restore noisy blurred image characters. This filter introduces an fuzzy decision mechanism to sharpen image features like edges and singularities while an anisotropic diffusion process is used to remove noise. A useful application of the proposed filter is the improvement of image segmentation and binarization task. Its efficiency on degraded document...
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