SAR imagedenoising has been an active research topic. Some imagedenoising methods have been implemented in both spatial and transform domains. Although state-of-the-art denoising methods are numerically impressive, ...
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ISBN:
(纸本)9781479989218
SAR imagedenoising has been an active research topic. Some imagedenoising methods have been implemented in both spatial and transform domains. Although state-of-the-art denoising methods are numerically impressive, they produce Gibbs-like phenomenon. To solve this problem, we proposed a SAR image denoising algorithm, which is based on dual-domain imagedenoising (DDID) and cycle-spinning algorithm. The proposed approach tries to change the relative position of singularity point in an image, to find the average spinning within a specific range and to smooth the artifacts due to the Gibbs-like phenomenon. Compared with DDID and wavelet method, the denoised SAR images of the proposed algorithm are smoother and have much fewer man-made textures.
imagedenoising is an under-determined problem, and hence it is important to define appropriate image priors for regularization. One recent popular prior is the graph Laplacian regularizer, where a given pixel patch i...
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ISBN:
(纸本)9781467369985
imagedenoising is an under-determined problem, and hence it is important to define appropriate image priors for regularization. One recent popular prior is the graph Laplacian regularizer, where a given pixel patch is assumed to be smooth in the graph-signal domain. The strength and direction of the resulting graph-based filter are computed from the graph's edge weights. In this paper, we derive the optimal edge weights for local graph-based filtering using gradient estimates from non-local pixel patches that are self-similar. To analyze the effects of the gradient estimates on the graph Laplacian regularizer, we first show theoretically that, given graph-signal h~D is a set of discrete samples on continuous function h(x;y) in a closed region Ω, graph Laplacian regularizer (h~D)~TLh~D converges to a continuous functional S_Ω integrating gradient norm of h in metric space G-i.e., ({nabla}h)~TG~(-1)({nabla}h)-over Ω. We then derive the optimal metric space G~*: one that leads to a graph Laplacian regularizer that is discriminant when the gradient estimates are accurate, and robust when the gradient estimates are noisy. Finally, having derived G~* we compute the corresponding edge weights to define the Laplacian L used for filtering. Experimental results show that our image denoising algorithm using the per-patch optimal metric space G* outperforms non-local means (NLM) by up to 1.5 dB in PSNR.
Traditional image denoising algorithms always assume the noise to be homogeneous white Gaussian distributed. However, the noise on real images can be much more complex empirically. This paper addresses this problem an...
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ISBN:
(纸本)9781467388528
Traditional image denoising algorithms always assume the noise to be homogeneous white Gaussian distributed. However, the noise on real images can be much more complex empirically. This paper addresses this problem and proposes a novel blind image denoising algorithm which can cope with real-world noisy images even when the noise model is not provided. It is realized by modeling image noise with mixture of Gaussian distribution (MoG) which can approximate large varieties of continuous distributions. As the number of components for MoG is unknown practically, this work adopts Bayesian nonparametric technique and proposes a novel Low-rank MoG filter (LR-MoG) to recover clean signals (patches) from noisy ones contaminated by MoG noise. Based on LR-MoG, a novel blind imagedenoising approach is developed. To test the proposed method, this study conducts extensive experiments on synthesis and real images. Our method achieves the state-of-the-art performance consistently.
A novel patch-based multi-view image denoising algorithm is proposed. This method leverages the 3D focus image stacks structure to exploit self-similarity and image redundancy inherent in multiple view images. Then a ...
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ISBN:
(纸本)9781509041183
A novel patch-based multi-view image denoising algorithm is proposed. This method leverages the 3D focus image stacks structure to exploit self-similarity and image redundancy inherent in multiple view images. Then a depth-guided adaptive window and dynamic view selection criterion is developed to aid proper selection of most consistent patches for the multi-view imagedenoising. Extensive experiments have been performed. Comparing the outcomes against those of state of the art image denoising algorithms, our proposed algorithm demonstrates significant performance advantage.
In our recent work we proposed an imagedenoising scheme based on reordering of the noisy image pixels to a one dimensional (1D) signal, and applying linear smoothing filters on it. This algorithm had two main limitat...
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ISBN:
(纸本)9781479903573
In our recent work we proposed an imagedenoising scheme based on reordering of the noisy image pixels to a one dimensional (1D) signal, and applying linear smoothing filters on it. This algorithm had two main limitations: 1) It did not take advantage of the distances between the noisy image patches, which were used in the reordering process;and 2) the smoothing filters required a separate training set to be learned from. In this work, we propose an image denoising algorithm, which applies similar permutations to the noisy image, but overcomes the above two shortcomings. We eliminate the need for learning filters by employing the nonlocal means (NL-means) algorithm. We estimate each pixel as a weighted average of noisy pixels in union of neighborhoods obtained from different global pixel permutations, where the weights are determined by distances between the patches. We show that the proposed scheme achieves results which are close to the state-of-the-art.
Purpose: This study aimed to acquire an image quality consistent with that of full-dose chest computed tomography (CT) when obtaining low-dose chest CT images and to analyze the effects of block-matching and 3D (BM3D)...
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Purpose: This study aimed to acquire an image quality consistent with that of full-dose chest computed tomography (CT) when obtaining low-dose chest CT images and to analyze the effects of block-matching and 3D (BM3D) filters on lung density measurements and noise reduction in lung parenchyma. Methods: Using full-dose chest CT images, we evaluated lung density measurements and noise reduction in lung parenchyma images for low-dose chest CT. Three filters (median, Wiener, and the proposed BM3D) were applied to low-dose chest CT images for comparison and analysis with images from full-dose chest CT. To evaluate lung density measurements, we measured CT attenuation at the 15th percentile of the lung CT histogram. The coefficient of variation (COV) and contrast-to-noise ratio (CNR) were used to evaluate the noise level. Results: The 15th percentile of the lung CT histogram showed the smallest difference between full- and low-dose CT when applying the BM3D filter, and the highest difference between full- and low-dose CT without filters (full-dose = - 926.28 +/- 0.32, BM3D = - 926.65 +/- 0.32, and low-dose = - 959.43 +/- 0.95) (p < 0.05). The COV was smallest when applying the BM3D filter, whereas the CNR was the highest (p < 0.05). Conclusions: The results of the study prove that the BM3D filter can reduce image noise while increasing the reproducibility of the lung density, even for low-dose chest CT.
Noise is an important factor which when get added to an image reduces its quality and appearance. So in order to enhance the image qualities, it has to be removed with preserving the textural information and structura...
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Noise is an important factor which when get added to an image reduces its quality and appearance. So in order to enhance the image qualities, it has to be removed with preserving the textural information and structural features of image. There are different types of noises exist who corrupt the images. Selection of the denoisingalgorithm is application dependent. Hence, it is necessary to have knowledge about the noise present in the image so as to select the appropriate denoisingalgorithm. Objective of this paper is to present brief account on types of noises, its types and different noise removal algorithms. In the first section types of noises on the basis of their additive and multiplicative nature are being discussed. In second section a precise classification and analysis of the different potential image denoising algorithm is presented. At the end of paper, a comparative study of all these algorithms in context of performance evaluation is done and concluded with several promising directions for future research work.
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