三尺度分解和稀疏表示的红外和可见光图像融合

为提高对含噪声源图像的处理效果,以及提高融合图像的对比度、结构信息等,提出一种基于三尺度分解和稀疏表示的红外和可见光图像融合算法。首先,为增强对噪声的去除效果,并且维持源图像的结构和边缘特性,利用滚动引导滤波器对源图像进行分解,将源图像分解为基础层和细节层;其次,为充分利用基础分量中的细节和能量,同时降低模型的复杂性,构造结构-纹理分解模型,将基础层再次分解为基础结构层和基础纹理层。然后分析三个分量的不同特点,使用不同的融合规则对三个分量分别进行融合,针对细节分量,其含有主要的噪声成分,但含噪声程度又不一样,因此根据图像含噪声的程度自适应确定稀疏融合去噪参数,从而同时实现对细节分量的融合和去噪,并且能够有效地提高计算效率;针对基础结构分量,其包含的细节特征较少,直接采用基于视觉显著图的加权平均技术进行预融合;针对基础纹理分量,由于其包含了视觉上重要的信息或图像特征,如边缘、直线和轮廓等活动信息,能够反映出原始基础图像的主要细节,因此采用主成分分析方法进行预融合,最终通过重构细节、基础结构和基础纹理层来得到融合图像。为验证所提方法的有效性,选取了多组红外和可见光图像进行试验,并与近期的五种方法CNN、FPDE、ResNet、IFEVIP、TIF进行了对比,采用主观和客观的形式对结果进行分析。实验结果表明,同其他图像融合算法进行对比分析,该方法能够兼顾含噪声和无噪声图像的融合,在有无噪声的情况下均能够将源图像的细节、亮度和结构保留到融合图像中,而且能有效地消除噪声。

基于Kalman滤波的信息融合白噪声最优反卷积滤波器

应用Kalman滤波方法 ,基于Riccati方程 ,在线性最小方差最优信息融合准则下 ,提出了两传感器最优信息融合白噪声反卷积滤波器。同单传感器情形相比 ,可提高滤波精度。它可应用于石油地震勘探信号处理。一个信息融合Bernoulli

相关观测融合稳态Kalman滤波器及其最优性

对于带相关观测噪声和带不同观测阵的多传感器系统,用加权最小二乘(Weighted least squares,WLS)法提出了两种相关观测融合稳态Kalman滤波算法.其原理是用加权局部观测方程得到一个融合观测方程,它伴随状态方程实现观测融合稳态Kalman滤波.用信息滤波器证明了它们功能等价于集中式融合稳态Kalman滤波算法,因而具有渐近全局最优性,且可减少计算负担.它们可应用于多通道自回归滑动平均(Autoregressive moving average,ARMA)信号观测融合滤波和反卷积.两个数值仿真例子验证了它们的功能等价性.

不同空间频率Perlin噪声防御对抗攻击研究

对抗攻击(Adversarial Attacks)通过向深度神经网络模型输入中添加精心设计、难以察觉的攻击数据来干扰模型运行,使其产生错误输出.对抗攻击可能会带来非常严重的问题,对其进行有效防御意义重大.本文将具有不同空间频率特性的Perlin噪声加入模型输入,通过具有空间特性的噪声掩盖攻击数据并研究其对三种具有代表性的对抗攻击方法:快速梯度方法(Fast Gradient Signed Method,FGSM)、投影梯度下降方法(Projected Gradient Descent,PGD)和稀疏L1下降法(Sparse L1 Descent,SLD)的防御效果.结果显示:(1)Perlin噪声提升了模型的准确性和鲁棒性;(2)不同空间频率Perlin噪声的防御效果存在差异;(3)在面对SLD攻击时,Perlin噪声的防御效果优于使用无空间结构噪声的防御效果.上述结果表明,Perlin噪声提升了模型的准确性和鲁棒性,在面对SLD攻击时有良好的防御效果.

多维和多重递推辅助变量辨识算法

对带白色观测噪声的多维自回归(AR)模型,应用相关方法,分别提出了多维和多重递推辅助变量(RIV)算法,给出了未知AR参数的强一致估计;推广了一维RIV算法。提出了模型噪声和观测噪声方差的信息融合估值器,它们具有强一致性。一个仿真例子说明其有效性。

结合小波变换和双边滤波的SICM图像降噪算法

扫描离子电导显微镜(SICM)能够实现微纳米级的形貌测量,引起广大学者的关注研究。针对SICM形貌图像易受噪声污染,影响后续应用的问题,提出了一种基于小波分层阈值处理的双边滤波算法。针对SICM形貌图像的多特征融合噪声,采用伪中值滤波局部处理图像中的强斑点噪声,有机结合小波阈值去噪和双边滤波去除图像高频和低频噪声,最终得到去噪效果较好的形貌图像。通过仿真实验和实测实验进行多次验证,该算法对比中值滤波、双边滤波和小波去噪3种去噪算法,其峰值信噪比提升幅度均大于9.8%。实验表明该算法在SICM形貌图像去噪方面具有更大优势。

Application of Image Fusion Methods to Cell Imaging Processing

Aim To fuse the fluorescence image and transmission image of a cell into a single image containing more information than any of the individual image. Methods Image fusion technology was applied to biological cell imaging processing. It could match the images and improve the confidence and spatial resolution of the images. Using two algorithms, double thresholds algorithm and denoising algorithm based on wavelet transform,the fluorescence image and transmission image of a Cell were merged into a composite image. Results and Conclusion The position of fluorescence and the structure of cell can be displyed in the composite image. The signal-to-noise ratio of the exultant image is improved to a large extent. The algorithms are not only useful to investigate the fluorescence and transmission images, but also suitable to observing two or more fluoascent label proes in a single cell.

SELF-TUNING MEASUREMENT FUSION KALMAN FILTER WITH CORRELATED MEASUREMENT NOISES

For the multisensor system with correlated measurement noises and unknown noise statistics, based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variances and cross-covariances is obtained. Further, a self-tuning weighted measurement fusion Kalman filter is presented, based on the Riccati equation. By the Dynamic Error System Analysis (DESA) method, it rigorously proved that the presented self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion steady-state Kalman filter in a realization or with probability one, so that it has asymptotic global optimality. A simulation example for a target tracking system with 3-sensor shows that the presented self-tuning measurement fusion Kalman fuser converges to the optimal steady-state measurement fusion Kalman fuser.

INFORMATION FUSION STEADY-STATE WHITE NOISE DECONVOLUTION ESTIMATORS WITH TIME-DELAYED MEASUREMENTS AND COLORED MEASUREMENT NOISES

White noise deconvolution or input white noise estimation problem has important appli-cation backgrounds in oil seismic exploration,communication and signal processing.By the modern time series analysis method,based on the Auto-Regressive Moving Average(ARMA) innovation model,under the linear minimum variance optimal fusion rules,three optimal weighted fusion white noise deconvolution estimators are presented for the multisensor systems with time-delayed measurements and colored measurement noises.They can handle the input white noise fused filtering,prediction and smoothing problems.The accuracy of the fusers is higher than that of each local white noise estimator.In order to compute the optimal weights,the formula of computing the local estimation error cross-covariances is given.A Monte Carlo simulation example for the system with 3 sensors and the Bernoulli-Gaussian input white noise shows their effectiveness and performances.