基于非局部均值的彩色图像去噪

传统的彩色图像去噪算法通常是分层处理的,而忽略了彩色图像RGB通道之间的相关性,因此基于RGB通道联合相似度估计提出了一种新的彩色图像非局部均值去噪方法。在用非局部均值滤波对彩色图像进行去噪时,首先以目标像素为中心确定其支撑区域,然后根据多通道联合相似度估计确定权重,最后采用逐块滤波的方法对每一层进行滤波。并且针对彩色图像中含有的高斯噪声提出了一种新的噪声参数估计方法。由实验结果可以看出该算法比传统的去噪算法在PSNR和FSIM方面都有提高。因此可以看出在图像去噪过程中考虑三通道之间的相关性是必要的,同时也证明了算法的有效性。     

A novel denoising method for infrared image based on bilateral filtering and non-local means

This paper presents an image denoising method based on bilateral filtering and non-local means. The non-local region texture or structure of the image has the characteristics of repetition, which can be used to effectively preserve the edge and detail of the image. And compared with classical methods, bilateral filtering method has a better performance in denosing for the reason that the weight includes the geometric closeness factor and the intensity similarity factor. We combine the geometric closeness factor with the weight of non-local means, and construct a new weight. Experimental results show that the modified algorithm can achieve better performance. And it can protect the image detail and structure information better. This work has been supported by the Student’s Platform for Innovation and Entrepreneurship Training Program (No.201510060022). E-mail:lflian@tjut.edu.cn      

结合NSST和快速非局部均值滤波的刀具图像去噪

为消除基于图像处理的刀具磨损检测中的图像噪声,提出了结合非下采样Shearlet变换(Non-subsampled Shearlet Transform, NSST)和快速非局部均值(Fast Non-local Means, FNLM)滤波的图像去噪方法。首先,利用基于决策的非对称剪切中值(Decision Based Un-symmetric Trimmed Median, DBUTM)方法滤除图像中的椒盐噪声;然后,对图像进行NSST多尺度分解,得到一个低频子带和一系列高频子带;最后,分别使用FNLM滤波和各向异性扩散模型调整低频和高频子带系数,并由调整后的各子带系数重构出噪声滤除后的图像。实验结果表明,与基于小波的阈值收缩方法、基于Contourlet的全变差模型结合各向异性扩散方法、基于NSST和标准非局部均值滤波方法相比,本文方法在主观视觉去噪效果、峰值信噪比、结构相似度以及处理速度等4个方面性能更优。      

Image denoising based on non-local means filter and its method noise thresholding

Non-local means filter uses all the possible self-predictions and self-similarities the image can provide to determine the pixel weights for filtering the noisy image, with the assumption that the image contains an extensive amount of self-similarity. As the pixels are highly correlated and the noise is typically independently and identically distributed, averaging of these pixels results in noise suppression thereby yielding a pixel that is similar to its original value. The non-local means filter removes the noise and cleans the edges without losing too many fine structure and details. But as the noise increases, the performance of non-local means filter deteriorates and the denoised image suffers from blurring and loss of image details. This is because the similar local patches used to find the pixel weights contains noisy pixels. In this paper, the blend of non-local means filter and its method noise thresholding using wavelets is proposed for better image denoising. The performance of the proposed method is compared with wavelet thresholding, bilateral filter, non-local means filter and multi-resolution bilateral filter. It is found that performance of proposed method is superior to wavelet thresholding, bilateral filter and non-local means filter and superior/akin to multi-resolution bilateral filter in terms of method noise, visual quality, PSNR and Image Quality Index.     

Adaptive non-local means filter for image deblocking

Blocking artifacts often exist in the images compressed by standards, such as JPEG and MPEG, which causes serious image degradation. Many algorithms have been proposed in the last decade to alleviate this degradation by reducing the quantization noise. Unfortunately, these algorithms only produce satisfying results under an unreasonable assumption that noise magnitude has been given. However, in most applications, the user only gets inferior image copy, without any side information about noise distribution, therefore the efficiency of existing denoise algorithms is significantly reduced. In this paper, a new metric is first given to evaluate the blocking artifacts; and then non-local means filter is applied to remove quantization noise on the blocks. During the process, nonlocal means filters with different variances are used to do deblocking, and their efficiencies are recorded as the references. The deblocked image is finally the one combined with all blocks filtered with the optimal parameters. We prove with experimental results that the proposed algorithm constantly outperforms the peer ones on all kinds of images.     

基于双边滤波与非局部均值的图像去噪研究

提出了一种基于双边滤波与非局部均值的图像去 噪算法,近年来提出的非局部均值算法是去噪效果非常 出色的算法之一,双边滤波去噪算法采用空间邻近度和灰度相似性构造新的权重系数,其取 得了良好的去噪效果,本文 据此改进非局部均值算法的权重部分,把空间邻近度因子与非局部均值的权重系数相结合, 构造新的权重系数。实验表 明,本文改进权重的非局部均值算法与已有的去噪方法相比,能得到更好的峰值信噪比,能 更好的保护图像细节以及结构信息。     

非局部群稀疏表示的图像去噪模型

针对图像的非局部稀疏表示忽略图像中结构相似信息的缺点,将群稀疏表示引入到图像的最优滤波中,提出了一种有效去除图像高斯噪声的非局部群稀疏表示模型。该模型首先选择图像非局部相似块构建相似矩阵,在群稀疏限制下对相似矩阵进行正交分解得到正交矩阵;在已知噪声服从高斯分布的情况下,再通过求得的正交矩阵结合贝叶斯最小均方误差准则实现对特征矩阵的最优估计;最后通过正交矩阵与特征矩阵重构去噪后的图像。实验对比证明,所提的非局部群稀疏表示的图像去噪模型在去除噪声的同时更好地保留了图像的结构信息,获得了更好的主客观评价指标,去噪的峰值信噪比提高1 dB以上。     

Gaussian principle components for nonlocal means image denoising

NonLocal Means (NLM), taking fully advantage of image redundancy, has been proved to be very effective in noise removal. However, high computational load limits its wide application. Based on Principle Component Analysis (PCA), Principle Neighborhood Dictionary (PND) was proposed to reduce the computational load of NLM. Nevertheless, as the principle components in PND method are computed directly from noisy image neighborhoods, they are prone to be inaccurate due to the presence of noise. In this paper, an improved scheme for image denoising is proposed. This scheme is based on PND and uses preprocessing via Gaussian filter to eliminate the influence of noise. PCA is then used to project those filtered image neighborhood vectors onto a lower-dimensional space. With the preprocessing process, the principle components computed are more accurate resulting in an improved denoising performance. A comparison with some NLM based and state-of-art denoising methods shows that the proposed method performs well in terms of Peak Signal to Noise Ratio (PSNR) as well as image visual fidelity. The experimental results demonstrate that our method outperforms existing methods both subjectively and objectively.     

Curvelet based nonlocal means algorithm for image denoising

In this work, a curvelet based nonlocal means denoising method is proposed. In the proposed method, the curvelet transform is firstly implemented on the noisy image to produce reconstructed images. Then the similarity of two pixels in the noisy image is computed based on these reconstructed images which include complementary image features at relatively high noise levels or both the reconstructed images and the noisy image at relatively low noise levels. Finally, the pixel similarity and the noisy image are utilized to obtain the final denoised result using the nonlocal means method. Quantitative and visual comparisons demonstrate that the proposed method outperforms the state-of-art nonlocal means denoising methods in terms of noise removal and detail preservation.     

Gabor feature based nonlocal means filter for textured image denoising

The nonlocal means (NLM) filter has distinct advantages over traditional image denoising techniques. However, in spite of its simplicity, the pixel value-based self-similarity measure used by the NLM filter is intrinsically less robust when applied to images with non-stationary contents. In this paper, we use Gabor-based texture features to measure the self-similarity, and thus propose the Gabor feature based NLM (GFNLM) filter for textured image denoising. This filter recovers noise-corrupted images by replacing each pixel value with the weighted sum of pixel values in its search window, where each weight is defined based on the Gabor-based texture similarity measure. The GFNLM filter has been compared to the classical NLM filter and four other state-of-the-art image denoising algorithms in textured images degraded by additive Gaussian noise. Our results show that the proposed GFNLM filter can denoise textured images more effectively and robustly while preserving the texture information.     

基于同步噪声选择非局部平均除噪声的最优参数

提出基于同步噪声和信噪比（SNR）自动选择非局部平均（NCM）的核函数参数的算法。为了正确估计平滑图像中的剩余噪声,一幅纯噪声图像作为同步噪声与观测图像同步进行计算,同步噪声作为图像剩余噪声的估计值。为了保持同步噪声与剩余噪声的统计特性相似,提出了一种新的NCM算子,依据图像的特征对同步噪声进行平滑,能够保持两种噪声有...     

一种新的小波图像去噪方法

小波图像去噪已经成为目前图像去噪的主要方法之一，目前的研究主要集中于如何选取阈值使去噪达到较好的效果。边缘信息是图像最为有用的高频信息，在图像去噪的同时，应尽量保留图像的边缘信息，基于这一思想，提出一种新的小波图像去噪方法。用数学形态学算子对图像小波变换后的小波系数进行处理，以去除具有较小支持域的噪声，保留具有连续支持域的边缘。实验结果表明，与普通的小波阈值去噪方法相比，该方法不但可以保留图像的边缘信息，而且能提高去噪后图像的峰值信噪比2～5dB，提高信噪比6～10dB。     

基于广义变分正则化的红外图像噪声抑制方法

文中提出了一种广义变分正则化的红外图像噪声抑制方法,该方法采用p-范数代替目前广泛被采用的全变分范数作为正则项,构造了用于抑制图像噪声的展平泛函,从而将图像噪声抑制问题转化为能量泛函优化问题。通过推导,得到了相应的用于图像噪声抑制的非线性偏微分方程,并采用固定点迭代算法进行线性化求解,使得迭代解稳定收敛。数值试验结果表明,该方法能够有效地去除图像噪声,较之全变分图像噪声抑制方法,新方法进一步提高了对小宽度图像边缘的保持能力,是一种有效且性能优良的红外图像噪声抑制方法。     

基于统计特性的非局部均值去噪算法

针对非局部均值滤波的权值由相似块的欧式距来确定而未考虑其受噪声影响的缺点,提出了一种权值由相似块欧式距的统计特性确定的去噪算法。该算法首先对受到高斯噪声干扰的图像相似块的欧式距建立概率分布函数,再由概率分布函数确定权值大小,从而有效地减小高斯噪声对加权系数的影响,以提高去噪性能。实验中,从主客观方面与传统非局部均值滤波进行对比分析,实验数据表明本文提出的算法峰值信噪比提高约1dB,去除噪声的同时保留更多图像的细节信息,去噪性能更优。     

Depth image super-resolution algorithm based on structural features and non-local means

The resolution and quality of the depth map captured by depth cameras are limited due to sensor hardware limitations, which becomes a roadblock for further computer vision applications. In order to solve this problem, we propose a new method to enhance low-resolution depth maps using high-resolution color images. The structural-aware term is introduced because of the availability of structural information in color images and the assumption of identical structural features within local neighborhoods of color images and depth images captured from the same scene. We integrate the structural-aware term with color similarity and depth similarity within local neighborhoods to design a local weighting filter based on structural features. To use non-local self-similarity of images, the local weighting filter is combined with the concept of non-local means, and then a non-local weighting filter based on structural features is designed. Some experimental results show that super-resolution depth image can be reconstructed well by the process of the non-local filter and the local filter based on structural features. The proposed method can reconstruct much better high-resolution depth images compared with previously reported methods.     

一种改进的SAR图像去噪方法

在SAR成像过程中,由于散射的存在,SAR图像不可避免地会受到相干斑噪声的影响。为此,已经有了多种去除相干斑的方法。不过,增强型的Lee滤波在划分区域时没有考虑边缘的方向性。因此,针对增强型的Lee滤波检测边缘不具有方向性,在增强的Lee滤波算法的基础上,引入了Ratio边缘检测算子,将二者相结合,提出了一种改进的Lee滤波去噪方法。通过仿真实验,结果表明相对于一些传统的SAR图像滤波算法,此方法不仅具有良好的滤波效果,而且具备更好的边缘保持能力。     

基于非局部均值滤波的视频图像去雨方法

提出了一种有效的基于非局部均值滤波的去雨方法。首先利用雨的空间特征检测出视频帧中的雨线区域,并对其进行标记。然后针对当前帧的被标记的像素块使用非雨块匹配算法,用于相邻的帧之间以找出和有雨像素区域相似的块。最后使用时空非局部均值滤波对块中的雨区进行重构,达到去除雨滴的目的。实验结果表明,比起常规算法,文中提出的算法可以更有效地去除雨线,恢复出更高质量的图像。     

An image dependent stopping method for iterative denoising procedures

Iterative methods are very successful for denoising images corrupted by random valued impulse noise. However, choosing the optimal number of iterations is a difficult issue. In this letter, a stopping method is proposed: the iterative denoising process is stopped when the number of cleaned pixels is minimal. It corresponds to the moment when the denoising process tends to modify noise-free pixels. It also corresponds with a high precision to the maximum of PSNR of the restored image. The originality of the method is that no a priori iteration number is to be chosen but the method results from image information. The proposed stopping strategy is therefore an efficient and image dependent method that can be easily implemented on real data.     

一种改进的双变量收缩模型图像去噪

针对噪声图像低频子带含有噪声的特点,给出了一种改进的局部自适应双变量收缩模型的图像去噪算法,对于高频子带用局部自适应双变量模型进行去噪,而对低频子带用具有局部自适应的高斯模型进行去噪。该算法既体现了尺度内的聚类性,又体现了尺度间的相关性且具有很好的局部自适应性,在实验中用离散小波变换进行去噪。实验结果表明,这种改进的算法无论从峰值信噪比,还是从主观视觉效果上都要优于传统的去噪算法。     

一种改进的小波阈值裂纹图像去噪法

文中采用一种新的小波阈值法进行数字图像去噪。该方法是在原有小波算法之上进行改进,一方面改进闽值公式,寻求最优的阈值,获取更好的边缘保护,从而达到图像原始信息完整性;另一方面通过优化阁值函数获得改进的阚值方法,提高小波系数阈值判断的准确性,获得更好的去噪效果。实验结果表明,与通常的方法相比较,能更好的去除数字图像噪声、保护图像边缘,获得更好的峰值信噪比、边缘保持指数和更好的视觉效果,达到去噪要求。