Adaptive edge-preserving image denoising using wavelet transforms

Image denoising is a relevant issue found in diverse image processing and computer vision problems. It is a challenge to preserve important features, such as edges, corners and other sharp structures, during the denoising process. Wavelet transforms have been widely used for image denoising since they provide a suitable basis for separating noisy signal from the image signal. This paper describes a novel image denoising method based on wavelet transforms to preserve edges. The decomposition is performed by dividing the image into a set of blocks and transforming the data into the wavelet domain. An adaptive thresholding scheme based on edge strength is used to effectively reduce noise while preserving important features of the original image. Experimental results, compared to other approaches, demonstrate that the proposed method is suitable for different classes of images contaminated by Gaussian noise.     

基于最小Bayes风险的小波域局部自适应图像去噪

简要介绍了广义高斯分布的基本概念和小波系数的分布特性,分析了BayesShrink法的原理并指出其存在的不足。以冗余小波变换为基础,利用子带内小波系数之间的相关性,提出了一种局部自适应的图像去噪策略。以当前小波系数为中心,选取尺寸合适的邻域窗口,以该窗口为单位估计相应的理想标准差和局部阈值,再通过软化处理达到系数收缩的目的。实验表明,该算法能有效滤除图像噪声,较好地保留了图像纹理和细节等重要信息,取得了较高的峰值信噪比和较好的视觉效果。     

十字基自适应指导滤波在图像降噪中的应用

针对于传统线性滤波器在非边界区域与边界区域有着滤波平滑和边缘保护的矛盾,提出了一种基于指导滤波和十字基相结合的算法。其主要思想是对指导滤波加入自适应偏移量进行调整,保证在边缘结构处细节的保留;普通指导滤波的计算邻域为固定大小的方形窗,在自适应指导滤波算法邻域窗的选取中引入十字基滤波窗,以灰度相似性作为阈值,利用立体匹配的优势,生成大小可自动调节、形状可自适应调整的滤波块,从而实现了滤波性能的提高;偏移量的选取值随滤波窗灰度阈值正比变化,比硬阈值有更好的效果。通过对国际标准图像序列的实验仿真结果比较,表明十字基滤波窗在分块方面有着较高的效率,平滑区域的滤波效果更佳,偏移量的加入使算法在边界区的纹理和细节特征得以保留;较之普通指导滤波算法,提出的十字基自适应指导滤波在峰值信噪比(PSNR)值上提高了约2 dB;通过对实拍图像的处理结果对比表明该算法在实际运用中有着更好的适用性。     

复Daubechies小波域HMT模型Bayesian图像去噪

提出了基于复Daubechies小波域隐马尔可夫树(SDW-HMT)模型Bayesian图像去噪算法,由于SDW小波是紧支撑、对称、正交小波,且具有近似线性相位,将其与HMT模型结合,能够更加准确地刻画小波系数的统计特征,在Bayesian图像去噪中获得很好的效果,仿真实例显示了所提算法的有效性。     

Preconditioned edge-preserving image deblurring and denoising

Preconditioned conjugate gradient (PCG) algorithms have been successfully used to significantly reduce the number of iterations in Tikhonov regularization techniques for image restoration. Nevertheless, in many cases Tikhonov regularization is inadequate, in that it produces images that are oversmoothed across intensity edges. Edge-preserving regularization can overcome this inconvenience but has a higher complexity, in that it involves non-convex optimization. In this paper, we show how the use of preconditioners can improve the computational performance of edge-preserving image restoration as well. In particular, we adopt an image model which explicitly accounts for a constrained binary line process, and a mixed-annealing algorithm that alternates steps of stochastic updating of the lines with steps of preconditioned conjugate gradient-based estimation of the intensity. The presence of the line process requires a specific preconditioning strategy to manage the particular structure of the matrix of the equivalent least squares problem. Experimental results are provided to show the satisfactory performance of the method, both with respect to the quality of the restored images and the computational saving.     

Underwater image enhancement using an edge-preserving filtering Retinex algorithm

Multimedia Tools and Applications - We develop a novel edge-preserving filtering retinex algorithm for single underwater image enhancement, in which gradient domain guided image filtering (GGF)...     

基于时域自适应滤波及非局部平均的夜视图像去噪算法

为了增强夜视图像的可视性和清晰度,提出了一种适用于夜视环境的视频图像去噪和增强算法。首先在噪声消除方面,提出了一种基于卡尔曼结构更新的运动自适应时域滤波器。然后利用带有自适应限幅阈值的伽马校正调整RGB直方图,来增加去噪视频图像的动态范围。最后,使用非局部平均(NLM)去噪滤波器消除剩余噪声。提出的方法能够直接用于颜色滤波阵列(CFA)原始视频图像,以便获得较低的内存消耗。在夜间环境(低于0.1 lux)下,利用2百万像素CMOS传感器进行了具体测试,实验结果表明,相比现有的夜视图像增强方法,提出的视频图像增强法在夜间环境下均表现出了更加良好的性能指标。     

一种基于小波变换边缘保护的图像融合算法

提出一种基于小波变换的像素级图像融合算法。采用小波系数局部模极大和加权局部能量分析相结合的方法融合高频成分;用加权局部能量分析融合尺度系数。算法获得的融合图像具有很强的视觉表现能力。此算法不需要设置阈值,具有较强的泛化能力。对多聚焦图像进行的融合实验结果表明,基于小波系数局部模极大和局部能量分析相结合的高频融合策略较好地再现了图像中各种边缘信息;基于加权局部能量估计的低频融合策略有效地去除了原图像的模糊。融合后的图像在客观评价和主观视觉效果上均有显著提高。     

A survey of edge-preserving image denoising methods

Reducing noise has always been one of the standard problems of the image analysis and processing community. Often though, at the same time as reducing the noise in a signal, it is important to preserve the edges. Edges are of critical importance to the visual appearance of images. So, it is desirable to preserve important features, such as edges, corners and other sharp structures, during the denoising process. This paper presents a review of some significant work in the area of image denoising. It provides a brief general classification of image denoising methods. The main aim of this survey is to provide evolution of research in the direction of edge-preserving image denoising. It characterizes some of the well known edge-preserving denoising methods, elaborating each of them, and discusses the advantages and drawbacks of each. Basic ideas and improvement of the denoising methods are also comprehensively summarized and analyzed in depth. Often, researchers face difficulty in selecting an appropriate denoising method that is specific to their purpose. We have classified and systemized these denoising methods. The key goal of this paper is to provide researchers with background on a progress of denoising methods so as to make it easier for researchers to choose the method best suited to their aims.     

双树复小波域的邻域自适应贝叶斯收缩去噪

为了更加高效去除图像采集或传输中引入的噪声,提出了一种基于双树复小波域的邻域自适应贝叶斯收缩的图像去噪方法,利用了双树复小波变换的平移不变性和更多的方向选择性的优点,并考虑了系数间的局部自适应邻域相关性,以尺度适合的窗口为单位估计相应系数的方差,利用滑窗求其平均作为整个子带的图像方差,通过贝叶斯收缩来处理小波系数,从而实现高效的图像去噪。实验结果证明,该方法取得了很高的峰值信噪比和更好的视觉效果,去噪性能优良。     

A denoising approach via wavelet domain diffusion and image domain diffusion

Multimedia Tools and Applications - This paper presents a new image denoising algorithm based on wavelet transform and nonlinear diffusion. Although the wavelet domain diffusion methods are very...     

Adaptive singular value decomposition in wavelet domain for image denoising

Image denoising is an important issue in image preprocessing. Two popular methods to the problem are singular value decomposition (SVD) and wavelet transform. Various denoising algorithms based on these two methods have been independently developed. This paper proposes an approach for image denoising by performing SVD filtering in detail subbands of wavelet domain, where SVD filtering is adaptive to the inhomogeneous nature of natural images. Comparisons were made with respect to both SVD-based filtering methods and wavelet transform-based methods.     

小波域的快速自适应图像修复算法

传统的基于偏微分方程的图像修复算法需要大量迭代,修复所耗时间较长,复杂度高。针对这一问题,提出了一种小波域的非迭代自适应图像修复算法。该算法对破损图像进行小波分解,找到待修复区域,根据待修复区域及其邻域像素值自适应选择修复模板大小,对修复模板内的像素值进行方向筛选,使修复过程严格按照等照度线方向行进,对修复后的图像进行小波重构。实验结果表明,该方法显著地缩短了修复时间,且对于图像的纹理细节、结构信息都达到了更好的修复效果。     

保边缘自蛇模型毫米波图像去噪

由于毫米波图像分辨率低并伴随有大量噪声,其图像边缘常被噪声污染或丢失,为此提出保边缘自蛇模型并应用于毫米波图像去噪。引入只在图像的边缘处具有最大值,且对噪声不敏感的非局部梯度,以此构造边缘停止函数,使其在图像边缘处接近0,而在平坦同质区域接近1;给出保边缘的自蛇模型迎风差分数字解法;提出毫米波图像去噪的定量评价算法性能指标。实验结果表明,该算法在等效视数和边缘清晰度性能指标上明显优于标准自蛇模型和保特征的各向异性扩散模型,具有较好的去噪效果和保边缘能力。     

An SVD-based image watermarking in wavelet domain using SVR and PSO

The paper presents a novel blind watermarking scheme for image copyright protection, which is developed in the discrete wavelet transform (DWT) and is based on the singular value decomposition (SVD) and the support vector regression (SVR). Its embedding algorithm hides a watermark bit in the low–low (LL) subband of a target non-overlap block of the host image by modifying a coefficient of U component on SVD version of the block. A blind watermark-extraction is designed using a trained SVR to estimate original coefficients. Subsequently, the watermark bit can be computed using the watermarked coefficient and its corresponding estimate coefficient. Additionally, the particle swarm optimization (PSO) is further utilized to optimize the proposed scheme. Experimental results show the proposed scheme possesses significant improvements in both transparency and robustness, and is superior to existing methods under consideration here.     

基于连续且自适应小波阈值的图像去噪方法

阈值的选取是小波域图像去噪的关键技术之一,但传统的阈值各有其缺陷。提出了一种连续可导的阈值量化方法和具有自适应性的阈值计算方法。用三次多项式在硬阈值的基础上插值,使新的阈值函数保持了连续性和可导性。按Birge-Massart规则分层计算阈值,使阈值具有良好的局部自适应性。通过实验表明,该方法去噪后的图像主观视觉效果和峰值信噪比均比传统算法优越。     

M带小波的子带自适应阈值除噪算法

在广义高斯分布（Generalized Guassian Distribution,GGD）模型最优软阈值的基础上,提出了一种基于M带小波变换的子带自适应图像除噪阈值确定方法,在阈值确定中,考虑了尺度因子、子带大小等因素的影响。采用软阈值除噪,算法简单实用。实验表明,对纹理丰富的图像,该文提出的除噪方法效果优于目前流行的其他算法。