一种基于分块压缩感知和迂回匹配追踪算法的图像重构方法

在使用离散余弦变换下的正交匹配追踪算法重构图像时,存在计算复杂度高,精确率低,对传感矩阵要求较为苛刻等问题.因此,利用迂回式匹配追踪算法(Detouring Matching Pursuit,DMP)的计算复杂度优势和分块压缩感知技术(Block Compressed Sensing,BCS),提出了一种基于BCS-DMP的图像重构方法.首先,对图像信号均匀分块并进行离散余弦变换,其次,采用DMP算法重构信号,对重构信号进行离散余弦逆变换重构图像,最后,采用均值滤波算法作平滑处理,减少图像块效应.3种不同类型的图像重构实验结果表明,当压缩比取0.2,0.3,0.4,0.5时,采用BCS-DMP算法重构图像的峰值信噪比高于基追踪、正交匹配追踪等算法,且在重构时间上有较大优势,说明BCS-DMP算法适用于图像重构.     

基于OOMP与A-T谱的滚动轴承故障特征提取

为解决变转速下正交匹配追踪(OMP)过度匹配和非正交投影的问题,提出优化正交匹配追踪(OOMP)。根据轴承故障振动信号的特性,构建组合时频原子字典与OMP匹配;将鲸鱼优化算法引入到OMP中选择与残差信号匹配的最优原子,实现信号重构和故障特征增强。为避免阶次追踪的缺陷,引入角度-时间(A-T)谱提取故障特征。试验验证,OOMP可有效增强轴承故障特征,A-T谱用于变转速下轴承故障特征提取效果良好。     

面向混合支撑集模型的分布式压缩感知重构算法

针对混合支撑集模型,研究了分布式压缩感知(DCS)的信号联合重构,提出了一种联合向前变步长正交匹配追踪(joint LAVSOMP)算法,该算法在信号重构过程中根据相邻次迭代重建信号的能量差,自适应地对向前参数进行动态调整,在信号重建精度与算法运行时间上取得平衡。进而,在该算法的基础上,提出了一种联合向前向后的变步长正交匹配追踪(joint FBVSOMP)算法,该算法有效降低了原子误选的几率,提高了信号重建的精度。试验结果表明,joint LAVSOMP算法的重构性能优于向前参数固定的联合向前正交匹配追踪joint LAOMP算法,而joint FBVSOMP算法具有更高的信号联合重构性能。     

一种滚动轴承振动信号的数据压缩采集方法

针对目前滚动轴承振动信号频带越来越宽,依据传统香农-内奎斯特采样定理进行数据采集时,将会得到巨量振动数据,对存储、传输和处理带来困难的问题,提出了一种滚动轴承振动信号的数据压缩采集方法。首先分析了振动信号在正交字典傅里叶基上的近似稀疏性,即可压缩性;然后融入振动信号在傅里叶基上稀疏性的结构信息,得到其优化的测量矩阵并进行压缩测量;最后基于压缩测量值采用正交匹配追踪算法对原始振动信号进行重构。通过仿真试验,结果表明,该方法既可以得到较高的信号压缩比又有着精确的信号重构性能,在不丢失振动信息的情况下,大大减少了原始振动数据量。     

基于整数小波变换与矩阵分解的感兴趣区域可逆水印算法研究

目的针对传统感兴趣区域水印算法抵抗几何攻击能力较弱的缺陷,提出一种基于IWT-Schur的感兴趣区域可逆水印算法。方法首先对载体图像做小波变换,筛选出各子带ROI系数,接着采用Arnold变换加密水印图像,加密水印图像做整数小波变换得到一系列分量。最后结合Schur分解,将水印各分量对应加至载体各子带的ROI。结果采用整数小波变换IWT与矩阵Schur分解的嵌入方式,使得含水印图像的视觉质量良好,算法实现容易。含水印图像没有受到干扰时检测到的水印与原水印一致,含水印图像受到攻击时,也表现出良好的性能,检测到的水印品质较好。结论实验证明,此方案水印提取正确,且感兴趣区域无损恢复。     

基于图像感兴趣区域的图像压缩方法

目的提出一种基于图像感兴趣区域的图像压缩方式,实现在减少图像存储空间时图像失真少的效果。方法采用眼动仪提取图像感兴趣区域,制作压缩掩码对图像进行分区压缩,非感兴趣区域采用DCT算法进行压缩,而感兴趣区域不做任何压缩处理直接保留原样。结果主客观评价实验表明:压缩后的图像失真较少,视觉观察效果好,且压缩后所占存储空间减半,方法简便,压缩效率高。结论结合眼动仪提取感兴趣区域的压缩方法优于基于Itti视觉模型的压缩方法,适合压缩多种类型的图像,具有较好的实用性。     

卫星应用中基于视觉显著性的感兴趣区域图像编码算法

为了满足遥感图像在卫星应用中有限信道下高保真传输的需求,本文提出了一种基于视觉显著性的感兴趣区域图像编码算法.首先,基于CCSDS标准的编码框架可以降低编码算法复杂度,便于实时实现,适合于卫星应用场合.其次,引入视觉注意机制,采用一种基于视觉显著性的自动提取算法提取感兴趣区域.最后,通过使用掩模来确定ROI区域,可以对任意形状的ROI区域进行编码.实验结果表明,与JPEG2000中的感兴趣区域图像编码算法相比,本文算法结构简单,便于实现;拥有优秀的主观视觉质量,符合人眼的感知,在高压缩比下能够为用户提供更多的信息.另外,本文算法可以作为CCSDS标准中的感兴趣区域编码算法.     

基于TV-泊松奇异积分联合先验模型的图像重构

目的针对当前图像重构算法容易产生过渡平滑图像纹理区域,使复原图像丢失大量纹理,降低重构图像视觉质量等缺陷,提出TV-泊松奇异积分联合先验模型耦合贝叶斯推理的图像重构算法。方法引入配分函数,结合TV函数,构造TV图像先验。定义泊松奇异积分先验,并将其嵌入到TV先验中,设计一种联合先验模型,控制图像纹理平滑度。基于高阶统计量技术,完善图像退化模型,并耦合先验模型,生成重构图像的最大后验估计MAP。引入优化最小原则,求解MAP,完成贝叶斯推理,获取重构图像。对文中算法复原图像纹理的关键参数进行优化,并研究分析该算法的用户响应。结果与当前图像重构算法相比,文中算法的复原视觉质量更高,能够较好地平衡噪声与纹理。在图像退化程度较大时,文中算法具有良好的用户响应。结论文中算法能够较好地同步保持图像边缘与纹理。     

无线传感器网络中一种改进的目标定位算法

将定位区域网格化,把目标定位问题转化为稀疏搜索问题,建立基于压缩感知的定位模型。为满足压缩感知理论对测量矩阵的约束等距性要求,采用LU分解的方法对测量矩阵进行正交化预处理。同时提出一种改进的正交匹配追踪算法恢复目标位置,并利用加权质心算法进一步提高了定位精度。实验结果表明,该方案平均定位误差小于其它算法。     

利用无线局域网遥测的3-D表面轮廓重构

提出一种基于IEEE820.11无线局域网(WLAN)遥测捕获图像的三维恢复算法,通过无线网络中的现场计算机获取远端物体双目图像,采用立体视觉法对双目图像的精确匹配,得到三维场景的精确重构恢复。实验结果表明,该算法能有效地实现双目图像匹配和恢复远端物体的三维表面信息。     

基于生成对抗网络的压缩感知图像重构方法

目的为了解决传统压缩感知图像重构方法存在的重构时间长、重构图像质量不高等问题,提出一种基于生成对抗网络的压缩感知图像重构方法。方法基于生成对抗网络思想设计一种由具有稀疏采样功能的鉴别器和具有图像重构功能的生成器组成的深度学习网络模型,利用对抗损失和重构损失2个部分组成的新的损失函数对网络参数进行优化,完成图像压缩重构过程。结果实验表明,文中方法在12.5%的低采样率下重构时间为0.009s,相较于常用的OMP算法、CoSaMP算法、SP算法和IRLS算法,其峰值信噪比(PSNR)提高了10～12 dB。结论文中设计的方法应用于图像重构时重构时间短,在低采样率下仍能获得高质量的重构效果。     

基于频率分级的指纹图像压缩算法

指纹图像是由黑白相间的脊线、谷线排列在一起而构成的特殊灰度图像,反复出现的反差边缘、周围的背景区域使得指纹图像具备低、中、高三种不同的频率成分,本文利用小波包变换和频谱分析提出了一种基于频率分级的指纹图像压缩算法,对包含能量最多的低频子图像,采用无损差分脉冲编码(DPCM)算法;对包含能量较少的中频子图像,采用嵌入式小波零数编码(EZW)算法;对包含能量最少的高频子图像,采用集合分裂嵌入块编码(SPECK)算法。仿真实验表明,本算法在保证重建质量的前提下,压缩比平均提高了21.4%左右,逼真度平均提高了6.25%左右。     

基于改进SL0算法的MSFA模式多光谱图像去马赛克方法

针对MSFA模式多光谱图像去马赛克算法精度较低和计算复杂等缺点,利用压缩感知理论在信号恢复方面的优势,提出一种新的光谱图像去马赛克算法。采用随机模式的多光谱滤波阵列MSFA获得马赛克图像,通过将MSFA采样值等效为压缩感知理论中的感知矩阵采样所得数据,将去马赛克问题转化为压缩感知稀疏信号恢复问题,并利用多光谱图像的谱间相关性,给出基于压缩感知框架的多光谱图像去马赛克模型,最后采用改进的光滑0范数算法求解去马赛克问题,得到重构的多光谱图像。客观评价指标显示,该算法的峰值信噪比值相较于克罗内克压缩感知和组稀疏两种算法有明显提高;主观评价结果表明,该算法能有效减少重构图像中的锯齿现象,具有更好的视觉效果。     

基于小波与三维离散余弦变换的视频图像压缩

提出一维小波与三维DCT（离散余弦变换）相结合的视频图像压缩算法,可有效地改善XYZ压缩方法在高压缩比时的性能。该方法不涉及运动估计和运动补偿,复杂度低。实验结果表明,这种算法能快速、高质量地压缩视频图像。     

JPEG基线算法对BP神经网络图像压缩处理的影响

根据BP神经网络图像压缩处理中,存在对图像信息高低频部分处理质量不同和边缘效应等问题,提出了采用JPEG基线算法于BP神经网络图像压缩处理结构中,建立了该系统。并采用灰阶Lena图像进行实验,通过实验分析发现,采用这种新的结构来处理图像,不仅可以得到较大的压缩比,而且具有较好的峰值信噪比。实验结果证明这种具有自适应性的图像处理方法,不仅可行,而且能高效、稳定地重建图像。     

High compressive ghost imaging method based on discrete cosine transform using weight coefficient matching

ABSTRACT

Ghost imaging can capture a scene without directly catching sight of the target, but in the case of high compression ratio, high-quality imaging is challenging at present. Here a ghost imaging method using weight coefficient matching based on discrete cosine transform (DCT) is proposed, in which the high-quality target images can be retrieved by obtaining the larger weight value in one-dimensional (1D) DCT spectrum. In the case of low sampling, the proposed method can not only acquire the spectral coefficients with large weight, but also put them in the correct position; eventually it can obtain the desired image by inverse discrete cosine transform of the spectrum. At the same sampling ratio, both simulation results and optical experiments show that the reconstructed image quality of the proposed method exhibits better performance. In addition, even the sampling ratio is as low as about 3%, the outline of the target image can still be roughly recognized.     

ANFIS‐EM approach for PET brain image reconstruction

In this article, for the reconstruction of the positron emission tomography (PET) images, an iterative MAP algorithm was instigated with its adaptive neurofuzzy inference system based image segmentation techniques which we call adaptive neurofuzzy inference system based expectation maximization algorithm (ANFIS‐EM). This expectation maximization (EM) algorithm provides better image quality when compared with other traditional methodologies. The efficient result can be obtained using ANFIS‐EM algorithm. Unlike any usual EM algorithm, the predicted method that we call ANFIS‐EM minimizes the EM objective function using maximum a posteriori (MAP) method. In proposed method, the ANFIS‐EM algorithm was instigated by neural network based segmentation process in the image reconstruction. By the image quality parameter of PSNR value, the adaptive neurofuzzy based MAP algorithm and de‐noising algorithm compared and the PET input image is reconstructed and simulated in MATLAB/simulink package. Thus ANFIS‐EM algorithm provides 40% better peak signal to noise ratio (PSNR) when compared with MAP algorithm. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 1–6, 2015     

Droplet Image Super Resolution Based on Sparse Representation and Kernel Regression

Microgravity and containerless conditions, which are produced via electrostatic levitation combined with a drop tube, are important when studying the intrinsic properties of new metastable materials. Generally, temperature and image sensors can be used to measure the changes of sample temperature, morphology and volume. Then, the specific heat, surface tension, viscosity changes and sample density can be obtained. Considering that the falling speed of the material sample droplet is approximately 31.3 m/s when it reaches the bottom of a 50-meter-high drop tube, a high-speed camera with a collection rate of up to 10⁶ frames/s is required to image the falling droplet. However, at the high-speed mode, very few pixels, approximately 48-120, will be obtained in each exposure time, which results in low image quality. Super-resolution image reconstruction is an algorithm that provides finer details than the sampling grid of a given imaging device by increasing the number of pixels per unit area in the image. In this work, we demonstrate the application of single image-resolution reconstruction in the microgravity and electrostatic levitation for the first time. Here, using the image super-resolution method based on sparse representation, a low-resolution droplet image can be reconstructed. Employed Yang’s related dictionary model, high- and low-resolution image patches were combined with dictionary training, and high- and low-resolution-related dictionaries were obtained. The online double-sparse dictionary training algorithm was used in the study of related dictionaries and overcome the shortcomings of the traditional training algorithm with small image patch. During the stage of image reconstruction, the algorithm of kernel regression is added, which effectively overcomes the shortcomings of the Yang image’s edge blurs.     

基于码率预分配编码技术的高分辨率航测图像压缩

由于需要对大面阵航空CCD相机带来的庞大航测图像数据进行压缩,在研究多种图像压缩算法的基础上提出了一种基于比特位平面编码的码率预分配图像压缩算法(RPCA)。首先将图像进行多级整数小波变换,以去除图像像素之间相关冗余。根据率失真理论并结合各个子带对图像重建质量的重要性原则,编码前事先确定每个子带在总码率一定的情况下各个子带在实际编码中应当分配的码率大小,再利用自适应MQ算术编码对每个子带比特平面进行熵编码,从而得到细致的嵌入式码流。实验仿真结果表明,该RPCA码率分配精准,图像压缩质量与JPEG2000标准相当,且支持无损到有损的任意倍率图像压缩,但复杂度低于JPEG2000标准,适合于硬件的高速实现。     

Lossless color medical image compression using adaptive block‐based encoding for human computed tomographic images

The advancement in medical imaging systems such as computed tomography (CT), magnetic resonance imaging (MRI), positron emitted tomography (PET), and computed radiography (CR) produces huge amount of volumetric images about various anatomical structure of human body. There exists a need for lossless compression of these images for storage and communication purposes. The major issue in medical image is the sequence of operations to be performed for compression and decompression should not degrade the original quality of the image, it should be compressed loss lessly. In this article, we proposed a lossless method of volumetric medical image compression and decompression using adaptive block‐based encoding technique. The algorithm is tested for different sets of CT color images using Matlab. The Digital Imaging and Communications in Medicine (DICOM) images are compressed using the proposed algorithm and stored as DICOM formatted images. The inverse process of adaptive block‐based algorithm is used to reconstruct the original image information loss lessly from the compressed DICOM files. We present the simulation results for large set of human color CT images to produce a comparative analysis of the proposed methodology with block‐based compression, and JPEG2000 lossless image compression technique. This article finally proves the proposed methodology gives better compression ratio than block‐based coding and computationally better than JPEG 2000 coding. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 227–234, 2013