Image representation using block compressive sensing for compression applications

The emerging compressive sensing (CS) theory has pointed us a promising way of developing novel efficient data compression techniques, although it is proposed with original intention to achieve dimension-reduced sampling for saving data sampling cost. However, the non-adaptive projection representation for the natural images by conventional CS (CCS) framework may lead to an inefficient compression performance when comparing to the classical image compression standards such as JPEG and JPEG 2000. In this paper, two simple methods are investigated for the block CS (BCS) with discrete cosine transform (DCT) based image representation for compression applications. One is called coefficient random permutation (CRP), and the other is termed adaptive sampling (AS). The CRP method can be effective in balancing the sparsity of sampled vectors in DCT domain of image, and then in improving the CS sampling efficiency. The AS is achieved by designing an adaptive measurement matrix used in CS based on the energy distribution characteristics of image in DCT domain, which has a good effect in enhancing the CS performance. Experimental results demonstrate that our proposed methods are efficacious in reducing the dimension of the BCS-based image representation and/or improving the recovered image quality. The proposed BCS based image representation scheme could be an efficient alternative for applications of encrypted image compression and/or robust image compression.     

Saliency-guided compressive sensing approach to efficient laser range measurement

The acquisition of laser range measurements can be a time consuming process for situations where high spatial resolution is required. As such, optimizing the acquisition mechanism is of high importance for many range measurement applications. Acquiring such data through a dynamically small subset of measurement locations can address this problem. In such a case, the measured information can be regarded as incomplete, which necessitates the application of special reconstruction tools to recover the original data set. The reconstruction can be performed based on the concept of sparse signal representation. Recovering signals and images from their sub-Nyquist measurements forms the core idea of compressive sensing (CS). A new saliency-guided CS-based algorithm for improving the reconstruction of range image from sparse laser range measurements has been developed. This system samples the object of interest through an optimized probability density function derived based on saliency rather than a uniform random distribution. Particularly, we demonstrate a saliency-guided sampling method for simultaneously sensing and coding range image, which requires less than half the samples needed by conventional CS while maintaining the same reconstruction performance, or alternatively reconstruct range image using the same number of samples as conventional CS with a 16 dB improvement in signal-to-noise ratio. For example, to achieve a reconstruction SNR of 30 dB, the saliency-guided approach required 30% of the samples in comparison to the standard CS approach that required 90% of the samples in order to achieve similar performance.     

压缩感知在多源图像融合中的应用

针对全采样传统图像融合方法中计算量大、时间复杂度高的问题,提出了一种基于压缩感知(CS)理论的多源图像融合模型。为满足一定的稀疏性,将源图像在过完备二维离散余弦变换(DCT)字典上进行稀疏表示,并通过随机观测得到待融合的观测值;在每一图像块上采用基于标准差的方法自适应地计算融合权值,加权合成融合后的观测值,然后利用改进步长的梯度追踪算法求解稀疏系数,得到最终融合图像。实验结果表明：与传统方法相比,提出的融合模型在减少计算量和存储容量的同时,能更好地从源图像中提取信息,获得效果较好的融合图像。     

Image compressive sensing via hybrid regularization combining centralized group sparse representation and deep denoiser prior

To effectively solve the ill-posed image compressive sensing (CS) reconstruction problem, it is essential to properly exploit image prior knowledge. In this paper, we propose an efficient hybrid regularization approach for image CS reconstruction, which can simultaneously exploit both internal and external image priors in a unified framework. Specifically, a novel centralized group sparse representation (CGSR) model is designed to more effectively exploit internal image sparsity prior by suppressing the group sparse coding noise (GSCN), i.e., the difference between the group sparse coding coefficients of the observed image and those of the original image. Meanwhile, by taking advantage of the plug-and-play (PnP) image restoration framework, a state-of-the-art deep image denoiser is plugged into the optimization model of image CS reconstruction to implicitly exploit external deep denoiser prior. To make our hybrid internal and external image priors regularized image CS method (named as CGSR-D-CS) tractable and robust, an efficient algorithm based on the split Bregman iteration is developed to solve the optimization problem of CGSR-D-CS. Experimental results demonstrate that our CGSR-D-CS method outperforms some state-of-the-art image CS reconstruction methods (either model-based or deep learning-based methods) in terms of both objective quality and visual perception.     

Image coding using lapped biorthogonal transform

The wireless sensor network utilizes image compression algorithms like JPEG, JPEG2000, and SPIHT for image transmission with high coding efficiency. During compression, discrete cosine transform (DCT)–based JPEG has blocking artifacts at low bit-rates. But this effect is reduced by discrete wavelet transform (DWT)–based JPEG2000 and SPIHT algorithm but it possess high computational complexity. This paper proposes an efficient lapped biorthogonal transform (LBT)–based low-complexity zerotree codec (LZC), an entropy coder for image coding algorithm to achieve high compression. The LBT-LZC algorithm yields high compression, better visual quality with low computational complexity. The performance of the proposed method is compared with other popular coding schemes based on LBT, DCT and wavelet transforms. The simulation results reveal that the proposed algorithm reduces the blocking artifacts and achieves high compression. Besides, it is analyzed for noise resilience.     

基于光滑0范数压缩感知的多光谱图像去马赛克算法

提出了一种基于压缩感知(CS)的多光谱滤波阵列(MSFA)的多光谱图像去马赛克算法(DMA)。 首先,通过将MSFA采样得 到马赛克图像的过程等效为CS理论中的感知矩阵采样的过程,并充分利用多光谱图 像的空间和谱间 相关性,通过在三维空间傅里叶基上对多光谱图像进行稀疏表示;然后由随机MSFA模式和CS 理论构造的测量矩阵对多光谱图像进行观测投影,最后采用CS重构算法求解0范 数下的最优化问 题,从而得到多光谱图像的稀疏表示系数。给出对算法性能的评估数据和Matlab仿真 图片。实验结果证明,本文算法的峰值信噪比(PSNR)值高于克罗内克CS(KCS)和组稀疏(GS)两种算法,且有效地减少了上述两种算法中出现的模糊现 象,改善了图像的视觉效果。     

基于改进正交匹配追踪算法的压缩感知雷达成像方法

运算复杂度高是基于压缩感知(CS)的雷达成像方法走向实用亟待克服的难题。该文利用雷达目标散射率分布的稀疏性,研究了基于改进正交匹配追踪(OMP)算法的2维联合压缩成像方法。首先建立了步进频雷达回波的稀疏表示模型,根据稀疏字典和压缩测量的2维可分离特性,提出一种改进的OMP算法用于雷达图像形成,大大提高了计算效率,并很容易扩展到其他贪婪类算法中。从理论上对几种CS成像算法的性能及资源需求进行了分析比较,表明所提供的算法相比常规的CS算法在存储量和计算量上均具有显著的优势,仿真及暗室数据实验验证了所提成像算法的有效性。     

A Method for Compact Image Representation Using Sparse Matrix and Tensor Projections Onto Exemplar Orthonormal Bases

We present a new method for compact representation of large image datasets. Our method is based on treating small patches from a 2-D image as matrices as opposed to the conventional vectorial representation, and encoding these patches as sparse projections onto a set of exemplar orthonormal bases, which are learned a priori from a training set. The end result is a low-error, highly compact image/patch representation that has significant theoretical merits and compares favorably with existing techniques (including JPEG) on experiments involving the compression of ORL and Yale face databases, as well as a database of miscellaneous natural images. In the context of learning multiple orthonormal bases, we show the easy tunability of our method to efficiently represent patches of different complexities. Furthermore, we show that our method is extensible in a theoretically sound manner to higher-order matrices (“tensors”). We demonstrate applications of this theory to compression of well-known color image datasets such as the GaTech and CMU-PIE face databases and show performance competitive with JPEG. Lastly, we also analyze the effect of image noise on the performance of our compression schemes.      

Halftone image data hiding with intensity selection and connection selection

In this paper, we propose two novel algorithms, namely intensity selection (IS) and connection selection (CS), that can be applied to the existing halftone image data hiding algorithms DHSPT, DHPT and DHST to achieve improved visual quality. The proposed algorithms generalize the hidden data representation and select the best location out of a set of candidate locations for the application of DHSPT, DHPT or DHST. The two algorithms provide trade-off between visual quality and computational complexity. The IS yields higher visual quality but requires either the original multi-tone image or the inverse-halftoned image which implies high computation requirement. The CS has lower visual quality than IS but requires neither the original nor the inverse-halftoned images. Some objective visual quality measures are defined. Our experiments suggest that significant improvement in visual quality can be achieved, especially when the number of candidate locations is large.     

一种基于加权稀疏编码的频域视觉显著性检测算法

 针对现有的基于频域的视觉显著性检测算法检测准确度不高的弱点,本文提出了一种基于加权稀疏编码的频域算法,旨在提高检测准确度的同时保持频域算法运算速度快的优势.在传统的稀疏编码算法基础上,本文根据各子码的增量编码长度来设置它们的权重,实现对图像的加权稀疏编码而不是直接对原始图像进行处理.最后,为了处理多维的稀疏编码信号,本文利用信息论的思想对最新发表的图像签名算法进行了多通道改进,以香农自信息的形式输出图像的显著性检测结果.在公开的人眼跟踪数据库上同9种流行算法的实验对比和对算法复杂度的分析证明了本文算法的有效性和快速性.     

基于异质局部特征的图像稀疏表示

为了有效描述图像的多角度视觉内容,提出一种将图像异质局部特征集通过稀疏学习映射为图像全局稀疏表示的新方法.该方法从不同的训练特征集中学习超完备视觉词典,经过局部稀疏编码、最大值合并、加权联接及归一化等一系列处理步骤融合多种局部特征的互补信息,最终形成一个高维稀疏向量来描述图像的多角度视觉内容.将其应用于基于内容的图像检索(CBIR)任务中,实验结果表明,这种基于异质局部特征学习而来的图像全局稀疏表示解决了单一局部特征集描述图像的局限性和高维局部特征集相似性度量时空复杂度高的问题.     

采用超分辨率重建提升压缩图像质量的方法

针对JPEG的中低码率压缩图像即高压缩率图像存在较严重的块效应以及量化噪声,提出了一种对JPEG标准压缩图像进行优化的重建-采样方法.该方法对JPEG压缩图像采用三维块匹配算法(BM3D)进行去噪,去除图像中存在的块效应和量化噪声,进而提高超分辨率重建的映射准确性,再使用外部库对去噪后图像进行基于稀疏表示的超分辨率重建,补充一定的高频信息,最后对重建后的高分辨率图进行双三次下采样,得到与原始图像大小一致的图像作为最终优化图像.实验结果表明,该方法在中低码率情况下能够有效地提高JPEG压缩图像的质量,对高码率压缩图像也有一定效果.     

基于压缩感知的正六边形CFA模式彩色图像去马赛克方法

针对基于四边形排列的去马赛克(Demosaicking)的 传统方法存在拉链现象和虚假色等问题,本文尝试将更加符合人眼视觉特性的六边形采 样方式应用于彩色图像成像 系统,并从图像稀疏特性角度出发,提出基于压缩感知(Compressive sensing,CS)框架的 彩色图像去马赛克方法。本文方法 充分挖掘了彩色分量间和分量内的稀疏特性,可使复原图像的纹理细节与色彩更加逼真,有 效地避免了拉链现象和虚假色现象。实验结果验证了本文方法的有效性。     

基于光谱稀疏模型的高光谱压缩感知重构

提出了一种基于光谱稀疏化的压缩感知采样与重构模型,通过从训练样本中构建光谱稀疏字典提升光谱稀疏化效果,同时在重构时兼顾空间图像的全变分约束进一步提升重构精度.对200波段AVIRIS高光谱场景进行压缩感知重构的实验表明,利用构建的光谱稀疏字典与传统的DCT字典和Haar小波字典相比光谱稀疏化效果明显提升,同时在25%采样下基于光谱稀疏字典几乎无差别重构出了高光谱图像,同样条件下在空间和光谱的精度与现有常用方法相比有较大的提升.     

压缩传感条件下红外和可见光图像融合技术的研究

为了提高夜间对目标的识别能力,红外和可见光图像融合技术被广泛应用到夜视系统中。使用压缩传感技术可以通过获取信号的少量线性投影来保留信号的完整信息,解决红外成像中红外探测器件与图像分辨率之间的矛盾。以压缩传感测量值作为图像内容特征,直接进行图像融合,可以减少重构误差和计算量。因此本文提出一种压缩传感条件下的红外和可见光图像融合算法。首先,本文算法同时考虑融合图像和原始图像的相似度和对原始图像特征的保留程度,提出一个新颖的代价函数。然后,采用L₁范数优化求解该代价函数,得到融合图像对应的稀疏系数。最后,利用字典和该稀疏系数重构为融合图像。通过和几种压缩传感条件下的融合算法比较,可以看出本文算法在主观视觉效果和客观评价方面均具有显著优势。该算法为压缩传感条件下的图像融合提供一种新的有效手段。      

压缩感知理论及其应用前景

压缩感知理论是近年来提出的一种基于信号稀疏性的新兴采样理论。与通常的数据采样定理不同,该理论提出可以用远远少于传统采样定理所需的采样点数或观测点数恢复出原信号或图像。本文主要阐述了压缩感知中信号的稀疏表示、测量矩阵的设计及信号的重构算法等基本理论,论述了该理论的广阔应用前景。     

基于稀疏表达的图像去噪方法研究

提出一种基于混合字典的图像稀疏分解去噪方法。使用小波包函数和离散余弦函数构成混合字典,采用匹配追踪算法对图像进行稀疏分解,提取含噪图像中的稀疏成分,最后利用稀疏成分进行图像重构,达到去除图像中噪声的目的。实验中与单一字典稀疏分解去噪算法进行了对比,结果表明,所提出的混合字典稀疏去噪算法可有效提取图像中的稀疏结构,改善重构图像的主客观质量。     

Beyond visual word ambiguity: Weighted local feature encoding with governing region

Typically, k-means clustering or sparse coding is used for codebook generation in the bag-of-visual words (BoW) model. Local features are then encoded by calculating their similarities with visual words. However, some useful information is lost during this process. To make use of this information, in this paper, we propose a novel image representation method by going one step beyond visual word ambiguity and consider the governing regions of visual words. For each visual application, the weights of local features are determined by the corresponding visual application classifiers. Each weighted local feature is then encoded not only by considering its similarities with visual words, but also by visual words’ governing regions. Besides, locality constraint is also imposed for efficient encoding. A weighted feature sign search algorithm is proposed to solve the problem. We conduct image classification experiments on several public datasets to demonstrate the effectiveness of the proposed method.     

UWB Echo Signal Detection With Ultra-Low Rate Sampling Based on Compressed Sensing

A major challenge in ultra-wide-band (UWB) signal processing is the requirement for very high sampling rate. The recently emerging compressed sensing (CS) theory makes processing UWB signal at a low sampling rate possible if the signal has a sparse representation in a certain space. Based on the CS theory, a system for sampling UWB echo signal at a rate much lower than Nyquist rate and performing signal detection is proposed in this paper. First, an approach of constructing basis functions according to matching rules is proposed to achieve sparse signal representation because the sparse representation of signal is the most important precondition for the use of CS theory. Second, based on the matching basis functions and using analog-to-information converter, a UWB signal detection system is designed in the framework of the CS theory. With this system, a UWB signal, such as a linear frequency-modulated signal in radar system, can be sampled at about 10% of Nyquist rate, but still can be reconstructed and detected with overwhelming probability. The simulation results show that the proposed method is effective for sampling and detecting UWB signal directly even without a very high-frequency analog-to-digital converter.     

一种基于稀疏表示的红外与微光图像的融合方法

根据人类视觉系统及信号的过完备稀疏表示理论,提出了一种基于稀疏表示的红外与微光图像融合算法。该方法首先把图像分割成部分重叠的图像块,由正交匹配追踪算法完成图像块的稀疏分解;然后采用最大值融合准则选择融合系数并完成图像块的重构,得到融合结果图像。实验结果表明,本文算法的融合效果优于小波变换法、Laplacian塔型方法以及PCA方法等传统融合方法。