Movie Denoising by Average of Warped Lines

Here, we present an efficient method for movie denoising that does not require any motion estimation. The method is based on the well-known fact that averaging several realizations of a random variable reduces the variance. For each pixel to be denoised, we look for close similar samples along the level surface passing through it. With these similar samples, we estimate the denoised pixel. The method to find close similar samples is done via warping lines in spatiotemporal neighborhoods. For that end, we present an algorithm based on a method for epipolar line matching in stereo pairs which has per-line complexity O(N) , where is the number of columns in the image. In this way, when applied to the image sequence, our algorithm is computationally efficient, having a complexity of the order of the total number of pixels. Furthermore, we show that the presented method is unsupervised and is adapted to denoise image sequences with an additive white noise while respecting the visual details on the movie frames. We have also experimented with other types of noise with satisfactory results.     

基于离散余弦变换的水平集算法研究

针对传统水平集算法只对含有加性噪声的图像有较好处理结果的缺点,提出了一种基于离散余弦变换（DCT）的水平集研究算法。首先以图像中的每一点为中心构造隶属于图像的一系列子图像,对这些子图像进行二维DCT变换得到其变换系数,并受Lee滤波器可以去除乘性噪声的启发对这些系数进行运算,得到去除噪声后的平滑图像,再将平滑图像梯度的递减函数作为水平集演化方程的速度停止项,最后根据水平集演化方程对图像进行演化运算。实验结果表明,该方法能够在抑制乘性噪声的同时较好的对图像进行分割。     

基于平稳小波变换的图像去噪方法

针对传统正交小波变换在图像去噪时存在的边缘失真,提出了一种基于平稳小波变换的图像去噪方法。使用系数关联法将图像小波分解后的高频分量像素标记为噪声和边缘,如果小波系数被标记为边缘,则保持其系数不变,否则采用基于邻域的方法进行系数收缩。当噪声方差较大时,收缩后最小尺度的高频分量中会存在一些孤立的亮点或暗点,借助次大尺度高频分量将其去除,对处理后的小波系数进行平稳小波反变换得到去噪图像。实验结果表明,本文方法能够在去除噪声的同时较好地保持图像的边缘,是一种有效的图像去噪方法。     

基于密度聚类的光条中心线提取方法

为了解决线结构光3维测量中噪声光斑对提取精度的影响，采用了密度聚类灰度重心提取算法提取激光光条中心线。该方法由中心线预提取以及中心线最终提取两阶段组成，预提取阶段实现对激光与光斑两者中心线的同时提取，最终提取阶段采用基于连通性的密度聚类算法完整保留激光中心线并剔除噪声光斑。在仿真实验阶段，对大小为600pixel×600pixel、含有激光中心线的图像进行了加噪处理，并使用提取结果与真实中心线之间各点的均方根误差以及运行时间作为考察标准进行了实验研究。结果表明，该方法与传统灰度重心法相比，在高亮度各向异性光斑、高亮度小面积光斑、高亮度点噪声图像的均方根误差分别降低了12.59pixel，15.12pixel和83.36pixel，时间复杂度分别提高了0.383s, 0.412s和0.416s。该方法与传统灰度重心法相比具有更高的提取精度、近似的时间复杂度，且对噪声光斑具有较好鲁棒性，可以在噪声光斑图像中完整提取出光条中心线。     

融合梯度及分数阶积分算子的SVM滤波

为了在滤除椒盐噪声时更好的保护图像特征信息 ,利用分数阶积分算子、梯度信息和SVM 设计了一种滤波方法 FG-SVM。先设计PCNN噪点检测模型,将检测的噪点及信号点对应位置分别标记为1和0, 生成标记图像;然后根据标 记图像,在噪声图像上对每一个以信号点为中心的5×5区域,用中心 点周围的像素灰度信息、分数阶积分算子及梯度信 息构建训练样本,训练SVM获得去噪模型;再取以噪点为中心的5×5 区域构建测试样本,作为SVM去噪模型的输入来 估计区域中心的灰度值;最后用SVM的估计值取代噪点的灰度值,得到去噪图像。仿真试验 表明,分数阶积分阶次取 1.7时,能获得最好的去噪效果。对含噪 1%的Lena、Pepper及Camer.去噪,FG-SVM 的PSNR比MPCNN分别提高了[4.19,3.64]dB,且去噪图像的边缘细节清晰。     

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.     

An image denoising algorithm for mixed noise combining nonlocal means filter and sparse representation technique

Nonlocal means (NLM) filtering or sparse representation based denoising method has obtained a remarkable denoising performance. In order to integrate the advantages of two methods into a unified framework, we propose an image denoising algorithm through skillfully combining NLM and sparse representation technique to remove Gaussian noise mixed with random-valued impulse noise. In the non-Gaussian circumstance, we propose a customized blockwise NLM (CBNLM) filter to generate an initial denoised image. Based on it, we classify the different noisy pixels according to the three-sigma rule. Besides, an overcomplete dictionary is trained on the initial denoised image. Then, a complementary sparse coding technique is used to find the sparse vector for each input noisy patch over the overcomplete dictionary. Through solving a more reasonable variational denoising model, we can reconstruct the clean image. Experimental results verify that our proposed algorithm can obtain the best denoising performance, compared with some typical methods.     

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.     

基于向量相似度的LPG-PCA 图像去噪算法

基于主成分分析的去噪算法在进行局部像素分组时,由于噪声具有不确定性和随机性,以欧氏距离 直接作为图像块相似性这一判断标准容易使得结果产生偏差。针对此问题,文中提出了一种基于向量相似度的 LPG-PCA 图像去噪算法,将向量相似度和欧氏距离相结合作为相似图像块的判断标准,优化了相似图像块的选取。 此外,在相似图像块样本数的选取方面采用自适应的数量选取方法,使得样本数的选取更加合理,进一步提高了图 像的去噪质量。实验结果表明所提算法在峰值信噪比和结构相似性方面均优于传统的LPG-PCA 图像去噪算法,且 对亚毫米波成像也具有一定的去噪效果。     

Geometric Features-Based Filtering for Suppression of Impulse Noise in Color Images

A geometric features-based filtering technique, named as the adaptive geometric features based filtering technique (AGFF), is presented for removal of impulse noise in corrupted color images. In contrast with the traditional noise detection techniques where only 1-D statistical information is used for noise detection and estimation, a novel noise detection method is proposed based on geometric characteristics and features (i.e., the 2-D information) of the corrupted pixel or the pixel region, leading to effective and efficient noise detection and estimation outcomes. A progressive restoration mechanism is devised using multipass nonlinear operations which adapt to the intensity and the types of the noise. Extensive experiments conducted using a wide range of test color images have shown that the AGFF is superior to a number of existing well-known benchmark techniques, in terms of standard image restoration performance criteria, including objective measurements, the visual image quality, and the computational complexity.      

Image denoising based on wavelets and multifractals for singularity detection.

This paper presents a very efficient algorithm for image denoising based on wavelets and multifractals for singularity detection. A challenge of image denoising is how to preserve the edges of an image when reducing noise. By modeling the intensity surface of a noisy image as statistically self-similar multifractal processes and taking advantage of the multiresolution analysis with wavelet transform to exploit the local statistical self-similarity at different scales, the pointwise singularity strength value characterizing the local singularity at each scale was calculated. By thresholding the singularity strength, wavelet coefficients at each scale were classified into two categories: the edge-related and regular wavelet coefficients and the irregular coefficients. The irregular coefficients were denoised using an approximate minimum mean-squared error (MMSE) estimation method, while the edge-related and regular wavelet coefficients were smoothed using the fuzzy weighted mean (FWM) filter aiming at preserving the edges and details when reducing noise. Furthermore, to make the FWM-based filtering more efficient for noise reduction at the lowest decomposition level, the MMSE-based filtering was performed as the first pass of denoising followed by performing the FWM-based filtering. Experimental results demonstrated that this algorithm could achieve both good visual quality and high PSNR for the denoised images.     

A new adaptive switching filter with directional impulse detection

A novel adaptive switching filter （ASF） based on directional detection is proposed for denoising the images that are highly corrupted by impulse noise. The proposed algorithm employs an efficient noise detection mechanism. It first employs an efficient method to estimate the differences between the current pixel and its neighbors aligned with 28 directions. The current noise pixel is replaced by a median or a mean value within an adaptive filter window with respect to different noise densities. Experimental results show that the proposed approach can not only achieve very low miss-detection ratio and false-alarm ratio even up to high noise corruption, but also preserve the detailed information of an image very well.     

快速高效去除图像椒盐噪声的均值滤波算法

针对常见滤除椒盐噪声算法需要使用阈值、运算时间长、去除噪声效果不理想等缺陷,提出了一种快速高效去除图像椒盐噪声的均值滤波算法。新算法对滤波窗口下的疑似噪声像素,有针对性地选择少数信号像素构成信号像素集合,取集合中的元素均值对疑似噪声像素进行滤波。实验结果表明,对于噪声密度为1%到99%的图像,新算法均具有良好的去除噪声能力和保持细节能力,而且整个算法耗费时间很少,因而具有较大的实用性。     

Gamma norm minimization based image denoising algorithm

Focusing on the issue of rather poor denoising performance of the traditional kernel norm minimization based method caused by the biased approximation of kernel norm to rank function,based on the low-rank theory,a gamma norm minimization based image denoising algorithm was developed.The noisy image was firstly divided into some overlapping patches via the proposed algorithm,and then several non-local image patches most similar to the current image patch were sought adaptively based on the structural similarity index to form the similar image patch matrix.Subsequently,the non-convex gamma norm could be exploited to obtain unbiased approximation of the matrix rank function such that the low-rank denoising model could be constructed.Finally,the obtained low-rank denoising optimization issue could be tackled on the basis of singular value decomposition,and therefore the denoised image patches could be re-constructed as a denoised image.Simulation results demonstrate that,compared to the existing state-of-the-art PID,NLM,BM3D,NNM,WNNM,DnCNN and FFDNet algorithms,the developed method can eliminate Gaussian noise more considerably and retrieve the original image details rather precisely.     

基于改进最小噪声分离变换的异常检测算法

为了降低噪声对高光谱异常检测结果的影响以及提高异常检测率,提出了一种基于改进最小噪声分离(MNF)变换的新型高光谱异常检测算法。首先对传统的MNF变换进行改进,采用加权邻域均值法对噪声矩阵进行估计,对邻域内每一个像元给予一个特定的权值,提高背景像元在邻域矩阵中的比例,进而抑制噪声像元的比例,通过差值计算提取噪声信息,然后应用改进的MNF变换对高光谱图像进行降维去噪处理,最后,将获取的低维去噪图像利用异常检测算法进行检测,并用真实的AVIRIS数据进行了测试。结果表明,该算法有更好的降维去噪效果,提高了异常检测率。     

A stochastic image denoising method based on adaptive patch-size

A new stochastic nonlocal denoising method based on adaptive patch-size is presented. The quality of restored image is improved by choosing the optimal nonlocal similar patch-size for each site of image individually. The method contains two phase. The first phase is to search the similar patches base on adaptive patch-size. The second phase is to design the denoising algorithm by making use of similar image patches obtained in the first step. The multiple clusters of similar patches for each pixel point are searched by using Markov-chain Monte Carlo sampling many times. Following, we adjust the patch-size according to the consistency of multiple clusters. This processing is repeated until we obtain the optimal patch-size and corresponding optimal patch cluster. We get the estimation of noise-free patch cluster by employing modified two-directional non-local method. Furthermore, the denoised image is obtained by using the method of superposition approach. The theoretical analysis and simulation results show that the method is feasible and effective.

     

一种从低分辨率图像序列获取高分辨率图像的算法

在某些应用领域,常常需要得到目标的高分辨率图像,考虑到在这些应用场合中,往往可以获得对同一景物或目标的多帧图像,本文提出了一种基于亚像素级图像配准与类似于中值滤波插值的从多幅低分辨率(LR)图像中获取一幅高分辨率(HR)图像的算法。算法首先采用梯度方法计算出LR图像之间的位移量,经过图像配准后,每个HR像素点被赋予其作用域内所有LR像点值的中值。仿真结果表明,该算法简单有效,既能提高图像分辨率,又能较好地去除非线性噪声。     

基于稀疏分解的指静脉图像去噪

手指静脉识别技术因其独特的优势,受到广泛的关注。然而由硬件系统获取的手指静脉图像常常含有严重的噪声、阴影等问题,所以对低质量的静脉图像的去噪成为了整个识别过程的关键。本文提出了一种基于稀疏分解的指静脉图像去噪新方法。基于稀疏分解的图像去噪是将含有噪声的图像信息进行稀疏分解,分解成稀疏成分和其他成分。其中的稀疏部分是有用信息,其他部分被认为是噪声,再由图像的稀疏部分重建原始信号,达到恢复原始信号并去除噪声的效果。本文根据指静脉图像的静脉的特点,应用高斯函数构造了过完备库。用合成图像和真实指静脉图像分别对新算法进行实验验证。实验结果证明,与传统的去噪算法相比,峰值信噪比提高1-2dB。      

Optimal spatial adaptation for patch-based image denoising.

A novel adaptive and patch-based approach is proposed for image denoising and representation. The method is based on a pointwise selection of small image patches of fixed size in the variable neighborhood of each pixel. Our contribution is to associate with each pixel the weighted sum of data points within an adaptive neighborhood, in a manner that it balances the accuracy of approximation and the stochastic error, at each spatial position. This method is general and can be applied under the assumption that there exists repetitive patterns in a local neighborhood of a point. By introducing spatial adaptivity, we extend the work earlier described by Buades et al. which can be considered as an extension of bilateral filtering to image patches. Finally, we propose a nearly parameter-free algorithm for image denoising. The method is applied to both artificially corrupted (white Gaussian noise) and real images and the performance is very close to, and in some cases even surpasses, that of the already published denoising methods.     

基于面阵三维成像激光雷达的目标点云分割技术

针对偏振三维成像系统的高效目标三维点云分割问题,提出一种多维信息融合的高效分割理念。系统采用高分辨率EMCCD相机作为面阵探测器,在一次成像过程中,可同时获得视场中的灰度图像以及三维点云数据。根据该成像特点,建立灰度图的像素坐标与点云数据像素坐标之间的点对点映射关系,结合粒子群优化算法的边缘分割方法,将灰度图中目标分割后的坐标信息映射到三维点云数据中,得到其三维点云数据。该方法将三维点云数据降维处理为二维图像处理,显著降低了计算复杂度,避免了点云数据误差对分割精度造成的影响。实验验证了多维数据融合目标三维点云分割方法的有效性。