# 探索各种降噪算法的结果评价

## 探索各种降噪算法的结果评价

### A novel MR image denoising via LRMA and NLSS

#### 作者

Chen, Z (Chen, Zhen) 1Fu, YL (Fu, Yuli) 1Xiang, YJ (Xiang, Youjun) 1Zhu, YH (Zhu, Yinhao) 1

SIGNAL PROCESSING

#### 出版时间

AUG 2021

#### 摘要

Nonlocal self-similarity has been proven to be a useful tool for image denoising. For MR image denois-ing, the method combining the nonlocal self-similarity with the low-rank approximation has been re-cently attracting considerable attentions, due to its favorable performance. Since the original low-rank approximation problem is difficult to be solved, the frequently used method is to use the nuclear norm minimization for the matrix low-rank approximation. However, the solution obtained by nuclear norm minimization generally deviates from the solution of the original problem. In this paper, an approach for MR image denoising is proposed by combining a novel nonlocal self-similarity scheme with a novel low-rank approximation scheme. In proposed approach, a similarity evaluation with respect to the noise is proposed in the patch matching stage. To approximate the original low-rank minimization problem, the propose approach minimizes trace-based operator at each step. Every minimization is solvable and used to approximate the original low-rank minimization. An algorithm is established for this approximation, as well. Experimental results show that the proposed approach has a superior performance, comparing with some of the low-rank approximation methods, in both the objective quality metrics and visual in-spections.

(c) 2021 Elsevier B.V. All rights reserved.

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### AUTOMATIC RANK ESTIMATION OF PARAFAC DECOMPOSITION AND APPLICATION TO MULTISPECT IMAGE WAVELET DENOISING

#### 作者

Zidi, A (Zidi, Abir) 1Marot, J (Marot, Julien) 2Bourennane, S (Bourennane, Salah) 2Spinnler, K (Spinnler, Klaus) 1

#### 摘要

There are two main contributions in this paper. Firstly, we estimate the rank for the truncation of the Parafae decomposition in an optimal sense. For this, we propose a least squares criterion and justify the choice of the fast Nelder-Mead method to minimize this criterion. Secondly, we combine the truncation of the Parafac decomposition with multidimensional wavelet packet transform. A single rank value is estimated for each decomposition level, which simplifies the implementation. We exemplify the proposed method with an application to multispectral image denoising: we study the performance of the proposed method based on Parafac decomposition, compared to ForWaRD.

#### 评估方式

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### Multidimensional Wiener filtering using fourth order statistics of hyperspectral images

#### 作者

Letexier, D (Letexier, Damien) 1Bourennane, S (Bourennane, Salah) 1

#### 摘要

In this paper we propose a new multidimensional filtering method based on fourth order cumulants to denoise of data tensor impaired by correlated gaussian noise. We overview the multidimensional Wiener filtering that overcomes the well known lower rank-(K-1,…, K-N) tensor approximation. But this method only exploits second order statistics. In some applications, it may be interesting to consider a correlated Gaussian noise. Then, we propose to introduce the fourth order statistics in the denoising algorithm. Indeed, the use of fourth order cumulants enables to remove the Gaussian components of an additive noise. Qualitative results of the improved multidimensional Wiener filtering are shown for the case of noise reduction in hyperspectral imagery.

#### 评估方式

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### Wavelet Based Iterative Thresholding for Denoising of Remotely Sensed Optical and Synthetic Aperture Radar Images

#### 作者

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#### 摘要

This article presents an overview of various denoising methods for optical and synthetic aperture radar (SAR) images. Currently, there are numerous algorithms and techniques to denoise images using adaptive filters in both the spatial as well as wavelet domain. However, in contrast, an algorithm which performs both soft and hard thresholding on a multi-level wavelet transformed image utilizing an adaptive threshold value has been designed and implemented. The threshold value varies for different wavelet regions from image to image and is selected for each region based on certain performance criteria such as Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). This algorithm has been used to denoise SAR images corrupted with multiplicative noise as well as optical images corrupted with White Gaussian additive noise. The results obtained have been compared with existing filters like median filter, Frost filter and Wiener filter. Additionally, we have compared the results of using different wavelet families including family of Daubechies and Biorthogonal filter banks.

#### 评估方式

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### New Method of Noise Removal in Images Using Curvelet Transform

#### 作者

Kumar, S (Kumar, Sumit) 1Biswas, M (Biswas, Mantosh) 1

#### 摘要

The term Curvelet transform in the field of Image Processing is quite well known from past few years. Its ability to detect curved features and smooth areas in an image marks its huge importance in the area of image denoising. However the ability to denoise image depends upon the selection and application of threshold after doing Curvelet based decomposition of an image. In this paper we are presenting our research methodology based on Curvelet transform image denoising. Our approach is based on the implementation of a modified window neighborhood processing that adapt itself based on the variance of neighboring pixels. We describe the problem we are considering for our research, present a brief overview of relative literature, describe the proposed methodology we have implemented and illustrate our future plan.

#### 评估方式

#### 参考

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## 总结

在被找到的一些文献中，图像的噪声处理评价略有区别，但大多相似，具体可参考 图像处理20210815–IQA图像质量评价