一种改进的红外图像自适应增强方法

对红外图像而言,如何在压缩动态范围的同时增强细节、抑制噪声以提升显示效果是一个重要的课题。文中提出一种改进的红外图像自适应增强方法,首先设计了一种参数自适应的引导滤波方法,并基于引导滤波将原始红外图像拆分成基本层和细节层;然后基于像素灰度分布设计了一种新型的自适应阈值的直方图映射方法,以对基本层压缩动态范围并增强其对比度;之后利用自适应引导滤波的线性系数对细节层进行增强并抑制噪声;最后对增强后的基本层和细节层进行自适应融合得到增强后的红外图像。实验结果表明,与对比度受限的自适应直方图均衡方法、基于引导滤波的高动态红外图像增强方法等几种效果相对较好的方法相比,文中所提出的方法处理后的图像细节更丰富,噪声抑制效果更强,视觉效果更好,且该方法适应性更强,无须调整参数即可应对多种观测场景。     

Adaptive polyphase subband decomposition structures for imagecompression

Subband decomposition techniques have been extensively used for data coding and analysis. In most filter banks, the goal is to obtain subsampled signals corresponding to different spectral regions of the original data. However, this approach leads to various artifacts in images having spatially varying characteristics, such as images containing text, subtitles, or sharp edges. In this paper, adaptive filter banks with perfect reconstruction property are presented for such images. The filters of the decomposition structure which can be either linear or nonlinear vary according to the nature of the signal. This leads to improved image compression ratios. Simulation examples are presented.     

Adaptive Linear Predictive Coding of Time-Varying Images Using Multidimensional Recursive Least Squares Ladder Filters

This paper presents several adaptive linear predictive coding techniques based upon extension of recursive ladder filters to two and three dimensions (2-D/3-D). A 2-D quarter-plane autoregressive ladder filter is developed using a least square criterion in an exact recursive fashion. The 2-D recursive ladder filter is extended to a 3-D case which can adaptively track the variation of both spatial and temporal changes of moving images. Using the 2-D/3-D ladder filters and a previous frame predictor, two types of adaptive predictor-control schemes are proposed in which the prediction error at each pel can be obtained at or close to a minimum level. We also investigate several modifications of the basic encoding methods. Performance of the 2D/3-D ladder filters, their adaptive control schemes, and variations in coding methods are evaluated by computer simulations on two real sequences and compared to the results of motion compensation and frame differential coders. As a validity test of the ladder filters developed, the error signals for the different predictors are compared and the visual quality of output images is verified.     

A tree-structured piecewise linear adaptive filter

The authors propose and analyze a novel architecture for nonlinear adaptive filters. These nonlinear filters are piecewise linear filters obtained by arranging linear filters and thresholds in a tree structure. A training algorithm is used to adaptively update the filter coefficients and thresholds at the nodes of the tree, and to prune the tree. The resulting tree-structured piecewise linear adaptive filter inherits the robust estimation and fast adaptation of linear adaptive filters, along with the approximation and model-fitting properties of tree-structured regression models. A rigorous analysis of the training algorithm for the tree-structured filter is performed. Some techniques are developed for analyzing hierarchically organized stochastic gradient algorithms with fixed gains and nonstationary dependent data. Simulation results show the significant advantages of the tree-structured piecewise linear filter over linear and polynomial filters for adaptive echo cancellation     

神经网络自适应有源噪声对消器

本文提出一种新颖的非线性自适应滤波器-递归神经网络(TNN)自适应滤波器．讨论RNN滤波器的构成及算法,实现了RNN自适应有源噪声对消器．RNN自适应有源噪声对消器性能优于由线性滤波器组成的线性对消器,能适应各种噪声环境．仿真结果充分证明了这一点．     

Nonlinear multivariate image filtering techniques

In this paper, nonlinear multivariate image filtering techniques are proposed to handle color images corrupted by noise. First, we briefly review the principle of reduced ordering (R-ordering) and then define three R-orderings by selecting different central locations. Considering noise attenuation, edge preservation, and detail retention, R-ordering based multivariate filters are designed by combining the R-ordering schemes. To implement color image filtering more effectively, we develop them into a locally adaptive version. The output of the adaptive filter is the closest sample to a central location that is a weighted linear combination of the mean, the marginal median, and the center sample. As a result, we study an adaptive hybrid multivariate (AHM) filter consisting of the mean filter, the marginal median filter, and the identity filter. The performance of the two adaptive filtering techniques is compared with that of some nonadaptive ones. The examples of color image filtering show that the adaptive multivariate image filtering gives a rather good performance improvement.     

一种改进的4.8kb／s码激励线性预测语音编码

本文介绍了码激励线性预测（ＣＥＬＰ）语音编码的基本原理，研究了一种制约随机激励的线性预测编码方案。它将随机激励码字进入合成滤波器的数量与自适应码本的性能指标联系起来，有效地减少了激励噪声对合成语音的影响。计算机模拟结果表明，这种方法在主观上改善了语音质量。     

Tree-structured nonlinear filters in digital mammography

A new class of nonlinear filters with more robust characteristics for noise suppression and detail preservation is proposed for processing digital mammographic images. The new algorithm consists of two major filtering blocks: (a) a multistage tree-structured filter for image enhancement that uses central weighted median filters as basic sub-filtering blocks and (b) a dispersion edge detector. The design of the algorithm also included the use of linear and curved windows to determine whether variable shape windowing could improve detail preservation. First, the noise-suppressing properties of the tree-structured filter were compared to single filters, namely the median and the central weighted median with conventional square and variable shape adaptive windows; simulated images were used for this purpose. Second, the edge detection properties of the tree-structured filter cascaded with the dispersion edge detector were compared to the performance of the dispersion edge detector alone, the Sobel operator, and the single median filter cascaded with the dispersion edge detector. Selected mammographic images with representative biopsy-proven malignancies were processed with all methods and the results were visually evaluated by an expert mammographer. In all applications, the proposed filter suggested better detail preservation, noise suppression, and edge detection than all other approaches and it may prove to be a useful tool for computer-assisted diagnosis in digital mammography.     

Robust adaptive recovery of spread-spectrum signals with short data records

The problem under consideration is the adaptive reception of a multipath direct-sequence spread-spectrum (SS) signal in the presence of unknown correlated SS interference and additive impulsive noise. An SS receiver structure is proposed that consists of a vector of adaptive chip-based Hampel nonlinearities followed by an adaptive auxiliary-vector linear tap-weight filter. The nonlinear receiver front end adapts itself to the unknown prevailing noise environment providing robust performance over a wide range of underlying noise distributions. The adaptive auxiliary-vector linear tap-weight filter allows rapid SS interference suppression with a limited data record. Numerical and simulation studies under finite-data-record system adaptation show significant improvement in bit-error-rate performance over the conventional linear minimum variance-distortionless-response (MVDR) SS receiver or conventional MVDR filtering preceded by vector adaptive chip-based nonlinear processing.     

A system identification algorithm using orthogonal functions

An adaptive filter (ADF) structure is proposed for applications in which large-order ADFs are required. It is based on modeling the impulse response of the system to be identified as a linear combination of a set of discrete Legendre orthogonal functions. The proposed adaptive filter structure has desirable stability features and a unimodal mean-square error surface as well as a modular structure that permits an easy increase of the filter order without changing the previous stages. Computer simulations in which the proposed structure is used to identify actual acoustic echo path impulse responses show that the Legendre ADF has better convergence performance than the transversal ADF when identifying systems with long impulse response     

一种全解耦的Volterra自适应滤波器

本文研究了Volterra自适应滤波的解耦问题.通过对Volterra滤波器的伪线性组合结构的提出和分析,得到了一个新的关于均方误差MSE的表达式,并将Volterra自适应滤波问题描述为一个约束优化问题,从而导出了满足最小均方误差(MMSE)指标的具有分块对角型输入相关矩阵的全解耦Volterra标准方程,据此设计了一种全解耦的Volterra自适应滤波器,给出了滤波器权向量的自适应修正公式.仿真结果验证了本文方法的有效性.     

利用线性相位模型提高干涉SAR图像相干性及改进相干性估计

本文提出一种对干涉合成孔径雷达(干涉SAR)处理中单视复图像(SLC)进行地形自适应预滤波的方法,提高从不同入射角获得的SAR图像之间的空间相干性,并同时改进相干性估计.通过Chirp-Z变换进行干涉图局部频率估计,从而提取干涉图线性相位模型,并利用其进行距离和方位向的地形自适应预滤波.由于滤波器参数随地形进行调整,其性能优于仅仅依赖轨道参数或平坦地球相位估计的固定带宽非自适应滤波器.文中对SIR-C/X-SAR的Etna火山干涉数据同时进行了距离和方位向地形自适应处理,利用相干系数直方图对该方法与固定带宽非自适应滤波方法的性能作了比较.最后,本文还应用线性相位模型改进相干性估计,得到了去除地形变化因素后的相干系数图.     

基于Nakagami分布的自适应斑点抑制与边缘增强方法

超声图像中的特殊斑点噪声严重影响了图像质量,针对此问题提出了一种基于Nakagami分布的自适应斑点抑制与边缘增强方法.根据斑点噪声的Nakagami分布模型,设计一个基于斑点局部统计特性的自适应滤波器.并应用"窄条"技术以不同方向与长度的"窄条"来近似图像的局部线性特性,滤波区域采用"窄条"代替常用的方形窗口,其中"窄条"的方向由假设试验优化方法确定,"窄条"长度与斑点的局部统计特性相关.实验证明,该方法在抑制斑点噪声、保留与增强图像边缘和细节方面均具有良好的性能.     

Adaptive two-pass rank order filter to remove impulse noise in highly corrupted images

In this paper, we present an adaptive two-pass rank order filter to remove impulse noise in highly corrupted images. When the noise ratio is high, rank order filters, such as the median filter for example, can produce unsatisfactory results. Better results can be obtained by applying the filter twice, which we call two-pass filtering. To further improve the performance, we develop an adaptive two-pass rank order filter. Between the passes of filtering, an adaptive process is used to detect irregularities in the spatial distribution of the estimated impulse noise. The adaptive process then selectively replaces some pixels changed by the first pass of filtering with their original observed pixel values. These pixels are then kept unchanged during the second filtering. In combination, the adaptive process and the second filter eliminate more impulse noise and restore some pixels that are mistakenly altered by the first filtering. As a final result, the reconstructed image maintains a higher degree of fidelity and has a smaller amount of noise. The idea of adaptive two-pass processing can be applied to many rank order filters, such as a center-weighted median filter (CWMF), adaptive CWMF, lower-upper-middle filter, and soft-decision rank-order-mean filter. Results from computer simulations are used to demonstrate the performance of this type of adaptation using a number of basic rank order filters.     

Design of linear equalizers optimized for the structural similarity index.

We propose an algorithm for designing linear equalizers that maximize the structural similarity (SSIM) index between the reference and restored signals. The SSIM index has enjoyed considerable application in the evaluation of image processing algorithms. Algorithms, however, have not been designed yet to explicitly optimize for this measure. The design of such an algorithm is nontrivial due to the nonconvex nature of the distortion measure. In this paper, we reformulate the nonconvex problem as a quasi-convex optimization problem, which admits a tractable solution. We compute the optimal solution in near closed form, with complexity of the resulting algorithm comparable to complexity of the linear minimum mean squared error (MMSE) solution, independent of the number of filter taps. To demonstrate the usefulness of the proposed algorithm, it is applied to restore images that have been blurred and corrupted with additive white gaussian noise. As a special case, we consider blur-free image denoising. In each case, its performance is compared to a locally adaptive linear MSE-optimal filter. We show that the images denoised and restored using the SSIM-optimal filter have higher SSIM index, and superior perceptual quality than those restored using the MSE-optimal adaptive linear filter. Through these results, we demonstrate that a) designing image processing algorithms, and, in particular, denoising and restoration-type algorithms, can yield significant gains over existing (in particular, linear MMSE-based) algorithms by optimizing them for perceptual distortion measures, and b) these gains may be obtained without significant increase in the computational complexity of the algorithm.     

Nonlinear adaptive IIR digital filter for linear system modeling

The structure and adaptive algorithm of a nonlinear adaptive IIR digital filter is presented. It does not need stability monitoring in the adaptive process, which has always been a computational burden and disturbs the adaptive process in linear adaptive IIR filtering. The individual parameter adaptation scheme is incorporated into the adaptive algorithm to optimally adjust each parameter at every iteration to improve convergence speed. Simulation results are conducted for linear IIR system modeling     

基于盲反卷积的盲自适应滤波器设计

通过盲反卷积的算法来实现盲自适应滤波,阐述了盲反卷积滤波器的工作原理及基本结构模型,通过调整滤波器系数来实现滤波,以便更好地跟踪信号的变化,最终实现自适应滤波,并借用Matlab仿真平台设计出自适应滤波器,验证了它的设计性能。     

LMS自适应滤波器的设计理论研究

主要介绍了自适应滤波器的基本理论思想,具体阐述了自适应滤波器的基本原理、算法及设计方法。首先介绍自适应滤波器的原理,然后对FIR结构滤波器做了详细的阐述,自适应算法是整个系统的核心,重点对LMS算法的设计方法,设计步骤做了分析,最后对LMS算法进行MATLAB仿真,根据性能评价标准和实验结果表明,该自适应滤波器滤波效果优越。