基于Cycle Spinning Contourlet变换和总变分最小化的遥感图像去噪算法

针对很多已有的遥感图像去噪算法去噪的同时存在不能有效的保留细节和增强边缘的问题,提出了一种基于Cycle Spinning Contourlet变换和总变分最小化的图像去噪新算法。该算法依据了Cycle Spinning Contourlet变换能够很好的保留原始图像的细节和纹理信息,而总变分最小化方法具有在去噪的同时增强图像边缘的特性,因此使用所提出的融合规则对两种算法去噪后的图像进行融合能够取得更好的增强效果。通过对比,实验结果表明该算法不仅能在很大程度上削弱分别由平移不变Contourlet变换和总变分最小化的图像去噪方法产生的伪吉布斯现象和阶梯效应,而且视觉效果和PSNR值均优于其它方法,同时该算法能够保留更多的光谱信息,因此该算法是一种有效的遥感图像去噪算法。     

基于Cycle Spinning Contourlet变换和总变分最小化的遥感图像去噪算法(英文)

针对大部分已有的遥感图像去噪算法在去噪的同时不能有效的保留细节和增强边缘,提出了一种基于Cycle Spinning Contourlet变换和总变分最小化的图像去噪新算法.该算法依据了Cycle Spinning Contourlet变换能够很好的保留原始图像的细节和纹理信息,而总变分最小化方法具有在去噪的同时增强图像边缘的特性,因此使用所提出的融合规则对两种算法去噪后的图像进行融合能够取得更好的增强效果.通过对比,实验结果表明该算法不仅能在很大程度上削弱分别由平移不变Contourlet变换和总变分最小化的图像去噪方法产生的伪吉布斯现象和阶梯效应,而且视觉效果和PSNR值均优于其它方法,同时该算法能够保留更多的光谱信息,因此该算法是一种有效的遥感图像去噪算法.     

New second order Mumford–Shah model based on Γ-convergence approximation for image processing

In this paper, a second order variational model named the Mumford–Shah total generalized variation (MSTGV) is proposed for simultaneously image denoising and segmentation, which combines the original Γ-convergence approximated Mumford–Shah model with the second order total generalized variation (TGV). For image denoising, the proposed MSTGV can eliminate both the staircase artefact associated with the first order total variation and the edge blurring effect associated with the quadratic H¹ regularization or the second order bounded Hessian regularization. For image segmentation, the MSTGV can obtain clear and continuous boundaries of objects in the image. To improve computational efficiency, the implementation of the MSTGV does not directly solve its high order nonlinear partial differential equations and instead exploits the efficient split Bregman algorithm. The algorithm benefits from the fast Fourier transform, analytical generalized soft thresholding equation, and Gauss–Seidel iteration. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed model.     

An Efficient Proximity Point Algorithm for Total-Variation-Based Image Restoration

In this paper, we propose a fast proximity point algorithm and apply it to total variation (TV) based image restoration. The novel method is derived from the idea of establishing a general proximity point operator framework based on which new first-order schemes for total variation (TV) based image restoration have been proposed. Many current algorithms for TV-based image restoration, such as Chambolle's projection algorithm, the split Bregman algorithm, the Bermúdez-Moreno algorithm, the Jia-Zhao denoising algorithm, and the fixed point algorithm, can be viewed as special cases of the new first-order schemes. Moreover, the convergence of the new algorithm has been analyzed at length. Finally, we make comparisons with the split Bregman algorithm which is one of the best algorithms for solving TV-based image restoration at present. Numerical experiments illustrate the efficiency of the proposed algorithms.     

Weighted Schatten p-Norm Low Rank Error Constraint for Image Denoising

Traditional image denoising algorithms obtain prior information from noisy images that are directly based on low rank matrix restoration, which pays little attention to the nonlocal self-similarity errors between clear images and noisy images. This paper proposes a new image denoising algorithm based on low rank matrix restoration in order to solve this problem. The proposed algorithm introduces the non-local self-similarity error between the clear image and noisy image into the weighted Schatten p-norm minimization model using the non-local self-similarity of the image. In addition, the low rank error is constrained by using Schatten p-norm to obtain a better low rank matrix in order to improve the performance of the image denoising algorithm. The results demonstrate that, on the classic data set, when comparing with block matching 3D filtering (BM3D), weighted nuclear norm minimization (WNNM), weighted Schatten p-norm minimization (WSNM), and FFDNet, the proposed algorithm achieves a higher peak signal-to-noise ratio, better denoising effect, and visual effects with improved robustness and generalization.     

一种基于信息保留网络的图像去噪算法

由于成像设备等各种因素影响, 图像在成像或传感过程中会受到噪声干扰。图像去噪旨在减少或消除噪声对图像的影响, 这一过程往往会导致高频信息的丢失。为了在去除图像噪声的同时保护图像的边缘信息与纹理细节, 文章提出了一种计算复杂度相对较低的含有信息保留模块的卷积神经网络, 直接对含噪声图像进行降噪。信息保留模块通过残差学习提取局部长路径和局部短路径的混合特征信息。该文采用峰值信噪比(PSNR/dB)和结构相似性(SSIM)两项评价指标对实验结果进行量化, 这两项指标值越大, 说明去噪效果越好。实验结果表明, 在峰值信噪比和结构相似性2项评价指标的均值可达到30.36 dB和0.828 0, 相比其他对比算法, 2项评价指标分别平均提升了2.15 dB和0.072 9。该算法对不同种类、不同水平的噪声都具有良好的去噪效果, 且速度优于所对比的一般算法, 对基于卷积神经网络的去噪工作的进一步发展有一定的作用。     

Semi-blind image restoration based on Chan-Vese denoising model

A semi-blind image restoration algorithm is proposed based on reduced non-convex approximation of Luminita Vese and Tony Chan's (C-V) denoising model. Compared with C-V denoising model, we modify the fidelity term and add a term on point spread function (PSF). The function depends on two variables: the image function to be restored u and the standard deviation of Gaussian kernel to be estimated σ. Then the problems consist in solving a system with two coupled equations. Compared with the Leah Bar's semi-blind image restoration model which must solve three coupled equations, our method only needs to solve two equations. Furthermore, the estimation of f by our algorithm is superior to Leah Bar's algorithm. The experimental results demonstrate that the proposed method is effective.     

一种基于双分支改良编解码器的图像去噪算法

针对传统图像去噪算法多噪声去除难,深层卷积神经网络去噪模型网络复杂、训练时间长等问题,提出一种基于自编码器结构的双分支改良编解码网络,实现高效图像去噪。双分支结构之一采用降-升采样实现点噪声消除,另一分支专注于宏观的图像修复和伪像去除,后端利用残差结构进行整合,实现数字图像混合噪声去噪。实验结果显示:对于含有标准差为15,均值为0的高斯噪声、噪声密度为5%的椒盐噪声和散粒噪声的混合噪声图像测试集,实验去噪效果相较于输入混合噪声图像峰值信噪比,平均提升了5.3%。与12层全卷积神经网络相比,去噪效果相当,训练速度提升了25.4%,体现了其“轻量级”的优点。实验表明:该方法相较于深层卷积神经网络,训练速度快,网络简单;相较于传统图像去噪算法,噪声去除效果也较为明显。该算法可应用于轻量级视觉平台后端去噪。     

A new kind of double image encryption by using a cutting spectrum in the 1-D fractional Fourier transform domains

Based on 1-D fractional Fourier transform, we proposed an image encryption algorithm in order to hide two images simultaneously. When the fractional order is closed to 1, most energy in frequency domain is centralized in the center part of spectrum. The image can be recovered acceptable by using a half of spectrum, which locates in the middle part at x-direction or y-direction. Cutting operation is employed in order to combine two spectra. Double random phase encoding is employed for image encryption. The corresponding numerical simulations are performed to demonstrate the validity and efficiency of the algorithm.     

基于广义变分正则化的闪光照相图像重建算法

 针对闪光照相图像信噪比低的特点,提出了一种基于广义变分正则化的图像重建算法,该方法采用p-范数取代目前广泛采用的全变分范数作为正则项,构造了用于图像重建的展平泛函,将图像重建问题转化为目标泛函最优化问题,采用固定点迭代法求解图像重建的最优解。数值计算结果表明,该算法在重建过程中能够有效抑制图像噪声,并加大对图像边缘的保持能力,从而提高了图像重建质量,是一种有效且性能优良的闪光照相图像重建算法。     

一种强噪声背景下微弱超声信号提取方法研究

为解决在强噪声背景下获取超声信号的难题,基于粒子群优化算法和稀疏分解理论提出一种强噪声背景下微弱超声信号提取方法.该方法将降噪问题转换为在无穷大参数集上对函数进行优化的问题,首先以稀疏分解理论和超声信号的结构特点为依据构建了粒子群优化算法运行所需要的目标函数及去噪后信号的重构函数,从而将粒子群优化算法和超声信号降噪联系在一起;然后根据粒子群优化算法可以在连续参数空间寻优的特点建立了用于匹配超声信号的连续超完备字典,并采用改进的自适应粒子群优化算法在该字典中对目标函数进行优化;最后根据对目标函数在字典上的优化结果确定最优原子,并利用最优原子按照重构函数重构出降噪后的超声信号.通过对仿真超声信号和实测超声信号的处理,结果表明本文提出的方法可以有效提取信噪比低至-4 dB的强噪声背景下的微弱超声信号,且和基于自适应阈值的小波方法相比本文方法表现出更好的降噪性能.     

基于植被特征库的高光谱植被精细分类

目前,高光谱数据精细分类面临两方面问题：一方面,传统单纯利用光谱信息的分类往往难以满足各应用行业对精度的需求,另一方面,基于像元的分类结果受制于椒盐噪声,影响其有效应用。为此,提出了一种基于植被特征库构建与优化的高光谱植被精细分类策略。首先,从高光谱影像中的原始光谱特征出发,结合灰度共生矩阵和局域指示空间分析两类纹理特征,并有针对性地加入了对植被叶绿素、胡萝卜素、花青素和氮素叶面积指数等理化参量敏感的光谱指数特征,构建了完备的植被特征库,以提高植被类别间的可分性;进而对植被特征库进行光谱维优化,提出了基于类对可分性的光谱维优化算法,选择对各类对具有最高识别能力的特征波段,通过迭代使各类别间均达到较高的区分度,并利用最优索引因子法进一步降低数据冗余,以提高分类效率;在进行植被特征库空间维优化时,主要基于地物分布通常具有一定的空间连续性这一理论,提出了基于邻域光谱角距离的植被特征库空间维优化算法,以去除分类结果中的椒盐噪声,提高分类精度和分类图像平滑度。基于航空高光谱数据的植被精细分类验证表明,该方法可以显著提高分类精度,在作物品种识别、精准农业等方面将具有广泛的应用前景。     

基于凸优化的傅里叶叠层成像技术研究

在傅里叶叠层成像（FPM）过程中采集的低分辨率图像会对重建图像质量产生直接影响,已有的研究提出用图像超分辨率重建技术和对低分辨率图像进行传统去噪处理的方法来解决该问题,但超分辨率重建的方法需要采集大量的原始图像,会加大采集端的时间损耗,而传统去噪算法会造成原始信息丢失,严重影响重构图像质量。因此论文引入凸优化算法,噪声图像的恢复可以通过求解一个凸优化模型来实现,并用迭代收缩阈值算法来求解该模型,算法中采用Barzilai-Borwein（BB）规则在每次迭代时初始化线搜索步长,加快收敛速度,选用软阈值函数,使图像去噪时原始信息丢失减少,最终重构图像的PSNR为27.634 6 dB,SSIM为0.926 1,所需处理时间为5.850 s,因此基于凸优化的傅里叶叠层成像技术具有时间损耗不大的情况下提高重构图像质量的优点。     

Implementing compressive fractional Fourier transformation with iterative kernel steering regression in double random phase encoding

This paper proposes a novel approach in double random phase encryption based on compressive fractional Fourier transform along with the kernel steering regression. The method increases the complexity of the image by using fractional Fourier transform and taking fewer measurements from the image data. Numerical results are given to analyze the validity of this technique. Considering natural images to be sparse in some domain, we apply a compressive sensing (CS) approach by using a TwIST algorithm. The encryption process has kernel steering regression algorithm for denoising and compressive sensing technique for image compression along with the fractional Fourier transform that makes the image in more complex form.     

中子数字图像几何不锐度校正算法研究

以中子数字成像系统的开发为背景,提出一种用于中子数字图像几何不锐度校正的图像复原算法。首先分析了中子数字成像的准直成像系统,得到引起中子数字图像几何不锐度的点扩展函数。据此,提出一种正则化的Lucy-Richardson(LR)算法,该算法利用贝叶斯(Bayes)最大后验估计理论研究了小波系数的双变量层间模型,推导出一种有效的小波降噪方法,并将小波降噪引入LR算法的迭代过程,此方法可有效解决原始LR算法的噪声放大问题。将改进的LR算法用于一个测试样品中子数字图像的几何不锐度校正,结果表明,该算法可以克服原始LR算法的不足,并优于频域小波域联合正则化图像复原算法。该方法还可以推广到其他图像复原的应用中。     

A weighted least squares algorithm for time-of-flight depth image denoising

In this paper, we present a new denoising method for the depth image of a time-of-flight (ToF) camera, based on weighted least squares (WLS) framework. The common method for ToF depth image denoising is to use bilateral filter. However, the ability of bilateral filter in edge preservation would be reduced while we attempt to smooth out larger spatial scale noise. In order to avoid this problem and preserve the edge information as much as possible, we introduce a new way to construct edge-preserving ToF depth image denoising based on WLS. We are to our knowledge the first to present a WLS-based method for ToF depth image denoising. Experimental results demonstrate that compared with bilateral filter, our proposed algorithm not only achieves better performance in edge preservation, but also improves the PSNR values of the denoised images by 0.5–2.6 dB.     

ARM架构下计算机图像并行化处理技术研究

为在图像处理与分析时具备良好的视觉效果,提高图像处理的速度,需要对ARM架构下计算机图像并行化处理技术进行研究。当前采用的方法是对各种变换频域图像特征提取与计算机图像集合特征的提取进行相结合,克服了当前对图像进行提取时存在图像形状描述的缺陷,提取图像特征向量维数相对较低。实验表明,通过对图像进行特征提取能很好的对图像效果进行展示,将图像的纹理特征进行详细的表述,将该方法应用到图像处理技术当中,具有良好的去噪效果及扩展性,该方法过程简单,但存在图像视觉效果较差的问题。为此,提出一种ARM架构下计算机图像并行化处理技术研究方法。该方法首先利用非局部均值去噪算法对图像进行去噪处理,然后结合图像去噪的结果利用小波变换对去噪图像进行边缘检测,最后采用非线性增强算法对图像进行增强完成对ARM架构下计算机图像并行化处理技术研究。实验结果表明,所提方法不仅提高图像处理速度,还提高图像视觉效果,具有广泛的应用价值。     

Logistic型混合自适应分数阶达尔文粒子群优化算法

针对分数阶达尔文微粒群优化(FDPSO)算法收敛速度慢,收敛精度不高的问题,改进其算法中分数阶速度更新策略,同时引入Logistic型混合分数阶自适应动态调整策略,得到一种改进的自适应分数阶达尔文粒子群优化(LFDPSO)算法,通过理论分析,证明了该算法在给定条件下的收敛性,并由数值实验表明,Logistic型混合自适应分数阶达尔文粒子群(LFDPSO)算法在收敛精度和收敛速度上得到了有效改善与提高,粒子在局部最优时的逃逸能力、全局寻优及智能搜索能力显著增强。     

离散分数阶随机变换与加权直方图交叉置乱的双图像加密算法

为了实现对两幅图像进行同步加密,降低传输负载并提高密文的抗明文攻击能力,提出了离散分数阶随机变换与加权像素混沌置乱的双图像加密算法。将2个分阶参数引入到Tent映射中,设计了新的Tent映射;根据明文像素值,构建加权像素直方图模型,联合位外部密钥,生成改进的Tent映射的初值;再利用初值对分数阶Tent映射进行迭代,输出2组随机序列,对2幅明文进行位置交叉混淆,获取2个置乱密文;基于DWT(discrete wavelet transform)技术,对2个置乱密文进行稀疏表示;根据混沌序列,定义随机循环矩阵,联合稀疏表示,获取2个置乱密文对应的测量矩阵。根据随机掩码与调制相位掩码,建立数据融合模型,将2个测量矩阵组合为复合矩阵;基于离散分数阶随机变换,对复合图像进行扩散,获取密文。测试数据显示:与已有的多图像加密方案相比,该算法的抗明文攻击能力与用户响应值更理想,密文的NPCR、UACI值分别达到了99.83%、34.57%。该算法具有较高的加密安全性,能够有效抵御网络中的外来攻击,确保图像安全传输。     

Double image encryption based on iterative fractional Fourier transform

We present an image encryption algorithm to simultaneously encrypt two images into a single one as the amplitudes of fractional Fourier transform with different orders. From the encrypted image we can get two original images independently by fractional Fourier transforms with two different fractional orders. This algorithm can be independent of additional random phases as the encryption/decryption keys. Numerical results are given to analyze the capability of this proposed method. A possible extension to multi-image encryption with a fractional order multiplexing scheme has also been given.