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PET allows functional imaging of the living tissue. However, one of the most serious technical problems affecting the reconstructed data is the noise, particularly in images of small animals. In this paper, a method for high-resol...
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PET allows functional imaging of the living tissue. However, one of the most serious technical problems affecting the reconstructed data is the noise, particularly in images of small animals. In this paper, a method for high-resolution small animal 3D PET data is proposed with the aim to reduce the noise and preserve details. The method is based on the estimation of the non-subsampled Haar wavelet coefficients by using a linear estimator. The procedure is applied to the volumetric images, reconstructed without correction factors (plane reconstruction). Results show that the method preserves the structures and drastically reduces the noise that contaminates the image.
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Deep learning techniques have received much attention in the area of image denoising. However, there are substantial differences in the various types of deep learning methods dealing with image denoising. Specifically, discriminat...
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Deep learning techniques have received much attention in the area of image denoising. However, there are substantial differences in the various types of deep learning methods dealing with image denoising. Specifically, discriminative learning based on deep learning can ably address the issue of Gaussian noise. Optimization models based on deep learning are effective in estimating the real noise. However, there has thus far been little related research to summarize the different deep learning techniques for image denoising. In this paper, we offer a comparative study of deep techniques in image denoising. We first classify the deep convolutional neural networks (CNNs) for additive white noisy images; the deep CNNs for real noisy images; the deep CNNs for blind denoising and the deep CNNs for hybrid noisy images, which represents the combination of noisy, blurred and low-resolution images. Then, we analyze the motivations and principles of the different types of deep learning methods. Next, we compare the state-of-the-art methods on public denoising datasets in terms of quantitative and qualitative analyses. Finally, we point out some potential challenges and directions of future research. (C) 2020 Elsevier Ltd. All rights reserved.
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Transform-domain image denoising methods assume that the original signal can be sparsely represented in the transform domain, but none of the orthogonal transforms can achieve sparse representation for all images. Proposed is a hy...
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Transform-domain image denoising methods assume that the original signal can be sparsely represented in the transform domain, but none of the orthogonal transforms can achieve sparse representation for all images. Proposed is a hybrid Fourier-wavelet denoising method to overcome this shortcoming. Experimental results show that the proposed algorithm improves denoising performance efficiently.
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The authors present a computationally efficient technique for maximum a posteriori (MAP) estimation of images in the presence of both blur and noise. The image is divided into statistically independent regions. Each region is mode...
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The authors present a computationally efficient technique for maximum a posteriori (MAP) estimation of images in the presence of both blur and noise. The image is divided into statistically independent regions. Each region is modelled with a WSS Gaussian prior. Classical Wiener filter theory is used to generate a set of convex sets in the solution space, with the solution to the MAP estimation problem lying at the intersection of these sets. The proposed algorithm uses an underlying segmentation of the image, and a means of determining the segmentation and refining it are described. The algorithm is suitable for a range of image restoration problems, as it provides a computationally efficient means to deal with the shortcomings of Wiener filtering without sacrificing the computational simplicity of the filtering approach. The algorithm is also of interest from a theoretical viewpoint as it provides a continuum of solutions between Wiener filtering and Inverse filtering depending upon the segmentation used. We do not attempt to show here that the proposed method is the best general approach to the image reconstruction problem. However, related work referenced herein shows excellent performance in the specific problem of demosaicing.
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Various image priors, such as sparsity prior, non-local self-similarity prior and gradient histogram prior, have been widely used for noise removal, while preserving the image texture. However, the gradient histogram prior used fo...
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Various image priors, such as sparsity prior, non-local self-similarity prior and gradient histogram prior, have been widely used for noise removal, while preserving the image texture. However, the gradient histogram prior used for texture enhancement sometimes generates false textures in the smooth areas. In order to address these problems, the authors propose a robust algorithm combining gradient histogram with sparse representation to obtain good estimates of the sparse coding coefficients of the latent image and realising image denoising while preserving the texture. The proposed model is solved by having a balance between over-enhancement and over-smoothing of the texture in order to preserve the natural texture appearance. Experimental results demonstrate the efficiency and effectiveness of the proposed method.
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Multiplicative noise is often present in medical and biological imaging, such as magnetic resonance imaging (MRI), Ultrasound, positron emission tomography (P
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Background Multi-b-valued/multi-shell diffusion provides potentially valuable metrics in breast MRI but suffers from low signal-to-noise ratio and has potentially long scan times. Purpose To investigate the effects of model-based ...
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Background Multi-b-valued/multi-shell diffusion provides potentially valuable metrics in breast MRI but suffers from low signal-to-noise ratio and has potentially long scan times. Purpose To investigate the effects of model-based denoising with no loss of spatial resolution on multi-shell breast diffusion MRI; to determine the effects of downsampling on multi-shell diffusion; and to quantify these effects in multi-b-valued (three directions per b-value) acquisitions. Study Type Prospective ("fully-sampled" multi-shell) and retrospective longitudinal (multi-b). Subjects One normal subject (multi-shell) and 10 breast cancer subjects imaging at four timepoints (multi-b). Field Strength/Sequence 3T multi-shell acquisition and 1.5T multi-b acquisition. Assessment The "fully-sampled" multi-shell acquisition was retrospectively downsampled to determine the bias and error from downsampling. Mean, axial/parallel, radial diffusivity, and fractional anisotropy (FA) were analyzed. Denoising was applied retrospectively to the multi-b-valued breast cancer subject dataset and assessed subjectively for image noise level and tumor conspicuity. Statistical Tests Parametric pairedt-test (P < 0.05 considered statistically significant) on mean and coefficient of variation of each metric-the apparent diffusion coefficient (ADC) from all b-values, fast ADC, slow ADC, and perfusion fraction. Paired and two-samplet-tests for each metric comparing normal and tumor tissue. Results In the multi-shell data, denoising effectively suppressed FA (-45% to -78%), with small biases in mean diffusivity (-5% in normal, +23% in tumor, and -4% in vascular compartments). In the multi-b data, denoising resulted in small biases to the ADC metrics in tumor and normal contralateral tissue (by -3% to +11%), but greatly reduced the coefficient of variation for every metric (by -1% to -24%). Denoising improved differentiation of tumor and normal tissue regions in most metrics and timepoints; subjectively, image noise level and tumor conspicuity were improved in the fast ADC maps. Data Conclusion Model-based denoising effectively suppressed erroneously high FA and improved the accuracy of diffusivity metrics. Evidence Level 3 Technical Efficacy Stage 1
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A self-adaptive smoothing method is proposed to deal with the displacement field obtained via digital image correlation (DIC). This new smoothing method is based on penalized least squares regression technique, which can penalize ...
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A self-adaptive smoothing method is proposed to deal with the displacement field obtained via digital image correlation (DIC). This new smoothing method is based on penalized least squares regression technique, which can penalize the roughness of the displacement and realize displacement denoising. In this method, generalized cross validation (GCV) method and discrete cosine transform (DCT) are used to get penalized parameter and true displacement from the noisy displacement field separately. This method has advantages of easy implementation, little calculation requirement, complete automation and so on. Both simulation analyses and experimental results validate the effectiveness of this smoothing technology, which can improve the strain distribution differentiated by the smoothed displacement without any calculation increasement.
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Statistical analysis has proven very successful in the image processing community. Linear methods such as principal component analysis (PCA) measure the degree of correlation in datasets to extract meaningful information from high...
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Statistical analysis has proven very successful in the image processing community. Linear methods such as principal component analysis (PCA) measure the degree of correlation in datasets to extract meaningful information from high-dimensional data. PCA was successfully applied in several applications such as image segmentation with shape priors and image denoising. The major assumption in these applications is that the dataspace is a linear space. However, this assumption is mainly wrong and as a consequence several non-linear methods were developed, among which diffusion maps were recently proposed. In this paper we develop a variational framework to compute the pre-image using diffusion maps. The key-problem of pre-image determination consists of, given its embedding, recovering a point. Therefore we propose to model the underlying manifold as the set of Karcher means of close sample points. This non-linear interpolation is particularly well-adapted to the case of shapes and images. We then define the pre-image as an interpolation with the targeted embedding. The new methodology can then be used for regularization in image segmentation as well as for shape and image denoising. We demonstrate our method by testing our new non-linear shape prior for shape segmentation of partially occluded objects. Further, we report results on denoising 2D images and 3D shapes and demonstrate the superiority of our pre-image method compared to several state-of-the-art techniques in shape and image denoising based on statistical learning techniques.
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Patch-based image denoising is a popular topic in recent years. In existing works, the distance between two patches was calculated as their Euclidian distance. When the noise level is high, this approach may not be desirable in im...
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Patch-based image denoising is a popular topic in recent years. In existing works, the distance between two patches was calculated as their Euclidian distance. When the noise level is high, this approach may not be desirable in image denoising. In this paper, we propose to extract noise-robust feature vectors from image patches and match the image patches by their Euclidian distance of the feature vectors for grey scale image denoising. Our modification takes advantage of the fact that the mean of the Gaussian white noise is zero. For every patch in the noisy image, we use lines to divide the patch into two regions with equal area and we take the mean of the right region for each line. Hence, a number of features can be extracted. We use these extracted features to match the patches in the noisy image. By introducing feature-based patch matching, our method performs favourably for highly noisy images.
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