直方图加权均值滤波器

本文提出了一种适合于消除图像盐椒噪声的滤波器——直方图加权均值(HWM)滤波器.该算法以加权均值滤波器为基础,利用被污染图像的直方图函数作为权值进行加权运算.实验表明,对于噪声率在5％到90％的的噪声图像,HWM滤波器具有良好而稳健的去噪效果,当噪声率超过70％时,其优越性更加突出.     

基于空间分布的红外图像直方图均衡算法

随着非制冷焦平面阵列(UFPA)红外探测器的日益成熟，红外热成像技术越来越广泛应用于公安、消防、军事、医学、监控等领域。红外图像直方图分布集中，对比度低并通常伴有大量散粒噪声，需要处理以改进视觉效果。文中提出一种基于空间分布的图像直方图均衡算法，在图像增强同时又适当抑制噪声提升，实验表明该算法优于HE、PE、HP等算法，并具有较快的计算速度。     

用于红外图像处理的多级滤波器VLSI结构设计

提出了一种多级滤波器的VLSI硬件实现结构。通过增加多分辨率处理,改善了多级滤波器的滤波效果;使用并行流水结构设计,提高了该滤波器数据运算的吞吐率。使用SMIC的0.35μm单元库设计,芯片面积为6万等效门,工作频率为50MHz,满足红外序列图像的实时滤波要求。     

Multi-dimensional sub-band coding: Some theory and algorithms

A system is proposed in order to split a multi-dimensional signal into N sub-bands, which are then subsampled by N. Subsequent upsampling and filtering allows the recovery of the original signal. Main features are a good bandpass characteristic of the channels, automatic aliasing cancellation and spectral invariance of the overall system. The one dimensional case, known as the quadrature mirrir filter (QMF, [1]), is generalized for both the separable and the non-separable case. A parallel implementation, based on pseudo-QMF filters, is presented as an efficient way to split a signal into equal sub-bands.     

一种宽动态范围下的红外图像局部细节增强算法

针对红外图像在宽动态范围下压缩到窄动态范围时带来的图像细节显示模糊、暗处细节丢失以及对比度偏低等问题,提出了一种基于引导滤波分层的红外图像细节自适应增强算法。对背景层利用优化的CLAHE算法来进行压缩,对细节层利用脉冲噪声的多尺度检测并结合韦伯定理的方法进行压缩;解决前述的弱细节模糊以及暗部细节丢失问题。综合主、客观实验结果表明,相对于映射类、分层增强类以及Retinex和集成学习类增强算法,本文所提算法在背景层对比度和光照强度优化、细节层噪声抑制和弱细节增强上取得优异效果;在信息熵、PSNR和SSIM三种客观评测指标下综合效果最佳;处理速度达到150 f/s。在保证实时性的同时,不仅提高了图像的整体对比度,还突出了图像的局部细节,适用于宽温度范围下复杂环境的应用。     

Respiratory parameter estimation in non-invasive ventilation based on generalized Gaussian noise models

Modeling of respiratory system under non-invasive ventilation by using measured respiratory signals is of great interest in respiratory mechanics research area. Statistical processing techniques in the time-domain may be utilized as an alternative to the commonly used frequency-domain analysis to estimate model parameters. In this work, we propose using a generalized Gaussian distribution (GGD) to model the measurement noise in the respiratory system identification problem. The parameters of the GGD (i.e. the mean, the variance and the shape) are estimated by maximum likelihood (ML) and moment based estimators. However, the estimation error should also be taken into account which is in fact investigated as measurement innovations together with the measurement noise. Thus the Kalman iterations are applied with the help of the score function to compute the measurement innovations. Finally, the complete picture of the measurement noise and innovation analysis of the respiratory models is obtained which helped us to evaluate the non-Gaussian noise extension in the respiratory system analysis.     

Distortion adaptive Sobel filters for the gradient estimation of wide angle images

We introduce a set of distortion adaptive Sobel filters for the direct estimation of geometrically correct gradients of wide angle images. The definition of the filters is based on Sobel’s rationale and accounts for the geometric transformation undergone by wide angle images due to the presence of radial distortion. Moreover, we show that a local normalization of the filters magnitude is essential to achieve state-of-the-art results. To perform the experimental analysis, we propose an evaluation pipeline and a benchmark dataset of images belonging to different scene categories. Experiments on both, synthetic and real images, show that our approach outperforms the current state-of-the-art in both gradient estimation and keypoint matching for images characterized by large amounts of radial distortion. The collected dataset and the MATLAB code of the proposed method can be downloaded at our web page http://iplab.dmi.unict.it/DASF/.     

基于概率加权求和投影的多维信号检测技术研究

在多维信号检测中,通常需要采用投影法来进行降维处理,从而减少计算量,简化检测过程.本文在待投影的众多样本中只有一个样本可能为目标的情况下,提出了一种新的投影算法,它充分利用每个样本点是否为目标的概率信息来构造组合样本;推导了每个样本是否为目标的概率分配公式.作为验证,将其运用到了红外图像序列中微弱点状运动目标的检测领域,给出了其性能分析和仿真结果,并与常用的最大值投影算法进行了性能对比.结果表明,本文算法综合性能优于属本文算法特例的后者,并且对低信噪杂波比情况下的目标检测,其性能有较大的提高.     

Symmetric noncausal spatial models for 2-D signals with applications in stochastic texture modeling

In many one-dimensional (1-D) and two-dimensional (2-D) digital signal processing applications, auto-regressive (AR) models are very useful and powerful tools. Most of the development work done so far in 2-D AR modeling was limited to causal models. Recently, noncausal models have generated a great deal of interest because these models are a more natural choice for many applications in image processing. In this paper, we generalize the 1-D problems of noncausal linear-phase signal mdoeling and system modeling to their 2-D counterparts. The purpose of this paper is twofold. First, for homogeneous random fields, we introduce and investigate the 2-D symmetric (zero-phase) noncausal AR signal and system modeling problems. We then develop two computationally efficient algorithms for the determination of model parameters. Finally, we investigate an application in stochastic texture modeling and provide experimental results.     

基于运动矢量场的双迭代全局运动估计方法

提出了一种基于运动矢量场的双迭代全局运动估计方法，该方法用两个最小二乘迭代过程来去除局部运动区域对全局运动估计的干扰。第一个迭代使用一个递减的百分比阈值来排除局部运动区域；第二个迭代过程使用一个固定的绝对阈值来检测完整的伞局运动区域并估计运动模型参数。实验结果表明双迭代法进行伞局运动估计的结果更加准确和稳定。     

激光雷达距离像噪声抑制及方法评价

针对激光成像雷达距离像距离反常噪声的噪声抑 制问题,提出了一种改进环圈滤波(IDF)算法并进行了评价。首先,根据 激光雷达距离像噪声的构成,建立了一般化的距离像噪声仿真模型,模型中包含了地面、目 标和细小结构等成分,可模拟 距离反常、失落信息和内部噪声等噪声干扰;其次,提出了一种IDF噪声抑制算法的一般形 式,利用仿真模型对中 值滤波(MF)、IDF等噪声抑制算法进行了分析,对计算结果从整体、非异常 值和细小结构等多个方面, 评价了算法的噪声抑制能力和保护目标细节信息能力;最后,采用实测距离像对算法进行了 验证。研究结果表明,利用建立 的距离像噪声仿真模型,可以有效地评价不同噪声抑制算法的 能力;同时,利用IDF可 以根据目标特性选取校正系数,在满足算法对保护目标细节信息能力要求的前提下,提高噪 声抑制能力。     

High resolution two-dimensional minimum free energy AR spectral estimation

We extend the minimum free energy (MFE) parameter estimation method to 2-D fields. This 2-D MFE method may be used to determine autoregressive (AR) model parameters for spectral estimation of 2-D fields. It may also be used to provide AR models for texture synthesis. The performance of the technique for closely spaced sinusoids in white noise is demonstrated by numerical example. Better results can be achieved than with the multidimensional Levinson algorithm.     

用于非合作信号噪底估计的改进自回归算法

非合作接收中由于信道或接收机模拟器件的影响可能导致接收信号功率谱存在噪声基底不平坦现象,为便于划定检测门限需要先估计噪声基底再对原始功率谱进行修正.传统的非线性自回归滑动滤波算法采用固定阈值,仅适用于窄带单信号的噪底估计.改进算法通过增加调节系数实现对阈值的动态调节,并根据功率谱特征计算初始阈值和调节系数,提高了原算法的适应范围和工程实用性.仿真结果表明,改进算法对不同种类的信号具有普适性,能解决原算法无法适应的宽带多信号噪底估计.     

基于光谱空间信息的高光谱遥感图像混合噪声评估

综合利用高光谱图像的光谱信息和空间信息,提出了一种新的混合噪声评估方法.首先通过滤波算法进行图像中均匀图像块的自动选取;然后利用多元线性回归模型,将均匀图像块内像素点的信号值和噪声值进行分离,并实现了图像中加性、乘性噪声的粗评估;最后根据噪声模型构建似然函数,利用最大似然估计法求解噪声模型参数.通过仿真图像和真实高光谱图像进行实验,验证了该方法的准确性和鲁棒性.     

用于多小区联合检测的高精度信道估计算法

现有TD-SCDMA系统中的信道估计算法扩展到多小区信道估计时,由于对邻区信道估计精度低,严重影响多小区联合检测算法的性能.先分析传统信道估计算法应用于多小区信道估计时性能下降的原因,然后通过改变信道估计矩阵的结构分别提出了基于匹配滤波、最小均方误差及两者结合的3种适用于多小区联合检测的信道估计算法,进而对不同信道估计算法的估计精度进行了分析.仿真结果表明:3种新的信道估计算法均比传统的Steiner算法估计精度更高,并能有效地提升多小区联合检测下TD-SCDMA系统的性能.     

Direct method for rotation estimation from spherical images using 3D mesh surfaces with SPHARM representation

Since a large field of view obviously bears important advantages, the use of spherical images is becoming increasingly important in various computer vision and image processing applications. This paper presents a novel rotation estimation approach for spherical images based on 3D mesh representation of gray level intensity. Once the 3D meshes of the underlying spherical images are obtained, the 3D rotation can be estimated directly and efficiently, without feature extraction and matching process. Subsequently, we propose a direct method for 3D object rotation estimation using spherical harmonics representation with SVD decomposition and ICP algorithm for estimation refinement. Experimental results validate our approach and prove its suitability and robustness for rotation estimation. Moreover, it performs well against noisy images, brightness changes, image compression and occlusions. A comparative study of our proposed approach with four similar methods for 3D rotation estimation between spherical images, is realized to prove its effectiveness.     

Cheap Joint Probabilistic Data Association with Adaptive Neuro-Fuzzy Inference System State Filter for Tracking Multiple Targets in Cluttered Environment

The cheap joint probabilistic data association (CJPDA) with the adaptive neuro-fuzzy inference system state filter (ANFISSF) is presented for tracking multiple targets in the presence of low and high cluttered environments. The state update step of the CJPDA filter (CJPDAF) is realized with the ANFISSF instead of Kalman filter. The adaptive neuro-fuzzy inference system (ANFIS) has the advantages of expert knowledge of fuzzy inference system and learning capability of neural networks. A hybrid learning algorithm, which combines the least square method and the backpropagation algorithm, is used to identify the parameters of ANFIS. The tracks estimated by using the method proposed in this paper for different tracking scenarios are in very good agreement with the original tracks.     

Multitarget state and track estimation for the probability hypotheses density filter

The particle Probability Hypotheses Density (particle-PHD) filter is a tractable approach for Random Finite Set (RFS) Bayes estimation, but the particle-PHD filter can not directly derive the target track. Most existing approaches combine the data association step to solve this problem. This paper proposes an algorithm which does not need the association step. Our basic ideal is based on the clustering algorithm of Finite Mixture Models (FMM). The intensity distribution is first derived by the particle-PHD filter, and then the clustering algorithm is applied to estimate the multitarget states and tracks jointly. The clustering process includes two steps: the prediction and update. The key to the proposed algorithm is to use the prediction as the initial points and the convergent points as the estimates. Besides, Expectation-Maximization (EM) and Markov Chain Monte Carlo (MCMC) approaches are used for the FMM parameter estimation.     

基于视点特征直方图的激光点云模型的位姿估计

提出一种基于视点特征直方图的点云模型位姿估计 算法。首先在目标物体周围采 集三维点云,拼接后获得物体的完整点云模型;然后对点云模型计算其视点特征直方图, 构建特征数据 库;对待估计点云同样计算其特征直方图,使用KNN算法在数据库中搜索与之最接近的位 姿作为初始位 姿估计值;最后使用迭代最近点(ICP)算法将待估计点云精确配准到模型点云,从而获得坐 标系之间的相 对位姿。实验表明,这种方法对于物体位姿识别有很强的稳健性,能很好实现目标物体的 三维位姿计算。     

A gradient-based optimization approach for reduction of blocking artifacts in JPEG images

This paper presents a gradient-based optimization approach to achieve reduction of blocking artifacts in compressed JPEG images. This approach involves decomposing a JPEG image into 1-D signals once along the rows or columns and once along the columns or rows. The reduction of blocking artifacts is carried out per 1-D signal by an optimization formulation where the gradient of an original 1-D signal is approximated based on the gradient of a compressed signal. A fixed-weight and an adaptive-weight optimization formulation are considered and solved analytically. A restored image is reconstructed by aggregating recovered 1-D signals. The performance of the developed method is assessed by examining both gray-level and color images and by computing the three measures of PSNR, SSIM, and GBIM. Comparison results with five existing methods are also reported.