基于总体最小二乘法的图像降噪

来自图像传感器的数字图像会受到各种噪声的干扰,其中主要包括加性噪声、乘性噪声和混合噪声。乘性噪声随信号幅度改变而改变,没有理想的去除方法。为此,运用基于总体最小二乘法的图像估计降噪方法,研究图像块尺寸选取对降噪性能的影响,分析成像系统中去马赛克环节影响噪声传播的内在规律,并通过比较实验给出总体最小二乘法降噪的性能优势。     

Derivation of centralized and distributed filters using covariance information

The problem of estimating a degraded image using observations acquired from multiple sensors is addressed when the image degradation is modelled by white multiplicative and additive noise. Assuming the state-space model is unknown, the centralized and distributed filtering algorithms are derived using the information provided by the covariance functions of the processes involved in the measurement equation. The filters obtained are applied to an image affected by multiplicative and additive noise, and the goodness of the centralized and distributed filters is compared by examining the respective filtering error variances.     

Statistical investigations of the synthetic aperture radar images

The focused and non-focused image-forming methods for airborne synthetic aperture radar (SAR) are investigated. The more precise receiving signal model which includes not only traditional additive noise but the multiplicative noise which is the consequence of the aircraft's velocity and height fluctuations is developed. Results of numerical calculations of the root-mean-square (rms) errors of the set of principal image parameters as functions of the additive noise variance and multiplicative noise standard deviation for several weighting windows and the histograms of the distributions of some image parameters are presented.     

Digital image enhancement and noise filtering by use of local statistics

Computational techniques involving contrast enhancement and noise filtering on two-dimensional image arrays are developed based on their local mean and variance. These algorithms are nonrecursive and do not require the use of any kind of transform. They share the same characteristics in that each pixel is processed independently. Consequently, this approach has an obvious advantage when used in real-time digital image processing applications and where a parallel processor can be used. For both the additive and multiplicative cases, the a priori mean and variance of each pixel is derived from its local mean and variance. Then, the minimum mean-square error estimator in its simplest form is applied to obtain the noise filtering algorithms. For multiplicative noise a statistical optimal linear approximation is made. Experimental results show that such an assumption yields a very effective filtering algorithm. Examples on images containing 256 × 256 pixels are given. Results show that in most cases the techniques developed in this paper are readily adaptable to real-time image processing.     

基于软门限去噪的图象压缩编码研究

在详细地分析了Donoho提出的子波域软限去噪方法的基础上,给出了含噪图象信号噪声水平的估计及门限值随尺度变化的规律。采用可分离的二维子波滤波器,方便地将Donoho的软门限去噪方法应用于图象信号处理,从而对含噪图象,在去除噪声的同时,又最大限度地进行了压缩。针对含噪的自然景物图象和合成孔径雷达图象的不同特点,分别提出了这在图象的压缩方案。对于SAR图象的压缩编码,通过一个自然对数变换,使得乘性噪声转变为适于软门限去噪的加性噪声。模拟结果显示,用软门限方法处理的解压缩图象比硬门限方法具有更好的视觉质量,因而该方法是解决含噪图象压缩编码的有效技术。     

Image restoration based on camera microscanning

Common restoration techniques use a single observed image for the processing. In this work three observed degraded images obtained from camera microscanning are utilized for image restoration. It is assumed that the degraded images contain information about an original image, multiplicative interference, and additive sensor’s noise. Using captured images a set of linear or nonlinear equations and objective function are formed. By solving the system of equations with the help of an iterative algorithm, the original image can be recovered. A fast algorithm for approximated image restoration is proposed. Computer simulations results presented and discussed.     

侧扫声呐图像的3维块匹配降斑方法

斑点噪声是影响侧扫声呐图像质量的主要因素,降斑处理对侧扫声呐图像的判别与分析非常重要。针对侧扫声呐图像自身特性和斑点噪声分布特点,提出一种基于3维块匹配(BM3D)的降斑方法。根据海底散射模型,得到侧扫声呐图像斑点噪声的瑞利分布模型,然后通过高斯光滑函数幂变换将瑞利分布的噪声转化为高斯分布,通过对数变换将乘性噪声转变为加性噪声,再进行自适应的BM3D滤波,最后采用逆变换得到降斑图像。实验结果表明,该方法在降噪、边缘和纹理保持等方面均优于空间域、小波域、Curvelet域的一些降斑方法。     

语音信号传输过程中的阈上随机共振现象

对利用阈上随机共振现象来改善语音信号的传输进行了研究.在理论上,选取输入输出互相关系数作为测度,用拉普拉斯信号模拟语音信号,研究了拉普拉斯信号在多阈值系统中受到加性高斯噪声和乘性高斯噪声作用下的阈上随机共振现象.在实际应用中,同样选取输入输出互相关系数作为测度,用真实的语音信号作为输入信号,在受到加性高斯噪声和乘性高斯...     

基于统计信息的数字图像恢复及噪声滤波方法

本文基于统计信息——局部均值和局部方差，探讨了数字图像对比度增强和噪声滤波方法。尤其对加性噪声和乘性噪声，设计了相应的滤波器。仿真结果表明，本文的方法实现简单、运算速度较快，为实时图像处理提供了一种新途径。     

改进的ROEWA算子用于SAR图像的海陆分离

加权指数平均比率（ROEWA）边缘检测算子是一种较好的适用于SAR 图像的边缘检测算子,但是使用梯度计算的方法不能准确地确定边缘的方向。针对这一问题,由于SAR图像受乘性噪声干扰,对图像取对数,将乘性噪声转化为加性噪声的形式,结合Canny算子计算边缘方向。根据陆地图像边缘丰富,海洋区域平滑的特点,以及陆地和海洋在灰度上的差异,结合区域生长完成海洋和陆地的分割。实验表明将改进的ROEWA算子用于海陆分离,检测效率和精度都比较高,且鲁棒性好。     

盲信号分离和序贯滤波的SAR影像水体提取

采用一种新的基于盲信号分离（BSS）和序列非线性滤波方法实现多极化合成孔径雷达（SAR）影像相干斑噪声抑制和水体目标快速提取。SAR影像具有强烈乘性相干斑噪声,影像数据为非高斯分布,但其具体分布形式及参数难以获得。利用基于独立分量分析的盲信号分离方法,不需要知道SAR影像的具体分布,通过对数量化将相干斑噪声转化为与图像数据相互独立的加性噪声,从多极化SAR影像中自动分离出图像数据与相干斑噪声,并自动选择相干斑指数最小的分量为图像分量。针对SAR影像水体目标的亮度及形状分布特征,进一步采用序列非线性滤波处理,从分离出的图像分量中提取出水体目标。利用ENVISAT ASAR多极化影像进行了实验,结果表明该方法可以快速准确地提取多极化SAR影像中的水体目标。     

基于改进的小波软阈值法的SAR图像去噪

斑点效应是SAR图像所不可避免的，在不降低图像空间分辨率前提下消除或最大限度地抑制斑点噪声，是当前研究的热点。本介绍了一条能够让小波技术更好地应用于雷达图像去噪而实现起来又非常简便的途径——将原始雷达图像作对数变换，将乘性噪声转化为加性噪声，再进行小波去噪。     

视觉机制引导的非均匀光照图像二次曝光

针对非均匀光照图像不能满足"视觉匹配"的问题, 依据人眼视觉机制提出了非均匀光照图像二次曝光算法. 首先, 融合退化过程模拟模型(Degradation process simulation model, DPSM)和Retinex模型得到了非均匀光照图像的成像模型, 利用修正的变分Retinex求解方法,获取乘性光照图像; 在人眼视觉阈值性的引导下去除加性光照图像, 获取反射图像; 依据视觉感光适应性对乘性光照图像进行动态范围调整, 并同反射图像相乘获取全局增强结果; 将全局增强结果同原始图像融合, 并对低照度区域进行颜色校正, 获取"视觉匹配"结果. 实验证明本文算法的场景再现结果可以较好地满足"视觉匹配", 性能达到或者超越了现有算法.     

A smoothing algorithm for systems with multiplicative noise

A fixed-point smoothing algorithm is derived for linear time-varying systems with multiplicative noise in the state channels of the plant and the measurements as well as additive noise. As in systems without multiplicative noise, the smoothed estimates depend on the filtered estimates. The steady-state behavior of the algorithm is examined. If the multiplicative part is absent, then the results coincide with known results for systems with additive noise     

Containment control for distributed networks subject to multiplicative and additive noises with stochastic approximation–type protocols

This paper is concerned with the stochastic containment control problem for distributed network with multiplicative and additive noises, in which the stochastic approximation technique is utilized. A major challenge, compared with the existing results with multiplicative and additive noises, is that the involving leaders are dynamically changing. It is shown that finite boundedness for dynamical leaders is of great importance for addressing the stochastic containment control issue. A suitable approach is proposed to handle multiplicative and additive noises based on the stochastic approximation, and some necessary/sufficient conditions are derived for mean square convergence with mild requirements. Furthermore, the convergence rates are studied for both dynamical leaders and static leaders scenarios. We conclude this paper by using two illustrative examples to verify the effectiveness of the theoretical results.     

Robust Image Restoration Algorithm Using Markov Random Field Model

A new method is proposed for image restoration of a gray-level image blurred by an erroneous point spread function and corrupted by either additive or multiplicative noise. The proposed method is based on a Markov random field model with an appropriate line field, whereby it has the ability to restore discontinuities. Robustness is incorporated by the total least-squares term in the posterior energy function. A simulated annealing algorithm is used to implement the proposed method. Simulation results comparing restoration based on minimizing posterior energy functions of type ℓ₂ ℓ₁, total ℓ₁, and total least squares with and without line field are presented.     

A new variational model for removal of combined additive and multiplicative noise and a fast algorithm for its numerical approximation

Variational image restoration models for both additive and multiplicative noise (MN) removal are rarely encountered in the literature. This paper proposes a new variational model and a fast algorithm for its numerical approximation to remove independent additive and MN from digital images. Two previous works by L. Rudin, S. Osher, and E. Fatemi [Nonlinear total variation based noise removal algorithms, Phys. D 60 (1992), pp. 259–268] and Z. Jin and X. Yang [Analysis of a new variational model for multiplicative noise removal, J. Math. Anal. Appl. 362 (2010), pp. 415–426] are used to develop the new model. As a result, developing a fast numerical algorithm is difficult because the associated Euler–Lagrange equation is highly nonlinear and standard unilevel iterative methods are not appropriate. To this end, we develop an efficient nonlinear multigrid approach via a robust fixed-point smoother. Numerical tests using both synthetic and realistic images not only confirm that our new model delivers quality results but also that the proposed numerical algorithm allows a very fast numerical realization of the model.     

A state variable approach to digital image processing

Digital filtering of images is considered. A recursive filter design for two-dimensional separable filters is discussed which is based on state variable methods. Advantages of this design are guaranteed stability, and shorter word length than filters designed by bilinear mapping techniques, greater computational efficiency and easier design as compared with nonrecursive filters. Results are shown for filtering high frequency additive noise from a low spatial frequency image and for filtering a low frequency multiplicative noise from a high spatial frequency image.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

Recursive operations in image algebra

The image algebra, developed by Ritter et al. at the University of Florida, is an algebraic structure for image processing. The three commonly used high-level image-template operations provided by the image algebra are the generalized convolution , the additive maximum or generalized lattice convolution {ie23-1}, and the multiplicative maximum {ie23-2}. These are used to realize various nonrecursive image transformations, including morphological transformations. Along with nonrecursive transformations, a class of recursive transformations, such as IIR filters, adaptive dithering, and predictive coding, are also widely used in signal and image processing. In this paper the notions of recursive templates and recursive template operations are introduced; these allow the image algebra to express a set of linear and nonlinear recursive transformations. Algebraic properties of these recursive operations are given, providing a mathematical basis for recursive template composition and decomposition. Finally, applications of recursive template operations in specifying some image processing algorithms are presented.