图像的多成分混合字典压缩感知表示及重构

信号分解的稀疏程度决定了压缩感知重构信号的精度,针对标准正交基稀疏程度的不足,提出了基于混合字典的压缩感知图像分解和重构方法。构建匹配图像边缘和纹理的二维Gabor字典,将图像在离散余弦字典与建立的二维Gabor字典上进行混合稀疏分解,得到图像的光滑成分、边缘成分和纹理成分。对得到的稀疏成分进行CS观测,通过求解一个优化问题重构图像。实验结果表明,构造的混合字典能够对图像进行更加稀疏的表示,在相同的采样率下,图像的重构质量优于标准正交基分解。     

Fast 2D discrete cosine transform on compressed image in restricted quadtree and shading format

Given a compressed image in the restricted quadtree and shading format, this paper presents a fast algorithm for computing 2D discrete cosine transform (DCT) on the compressed grey image directly without the need to decompress the compressed image. The proposed new DCT algorithm takes O(K²logK+N²) time where the decompressed image is of size N×N and K denotes the number of nodes in the restricted quadtree. Since commonly K<N, the proposed algorithm is faster than the indirect method by decompressing the compressed image first, then applying the conventional DCT algorithm on the decompressed image. The indirect method takes O(N²logN) time.     

Image categorization using a semantic hierarchy model with sparse set of salient regions

Image categorization in massive image database is an important problem. This paper proposes an approach for image categorization, using sparse set of salient semantic information and hierarchy semantic label tree （HSLT） model. First, to provide more critical image semantics, the proposed sparse set of salient regions only at the focuses of visual attention instead of the entire scene was formed by our proposed saliency detection model with incorporating low and high level feature and Shotton＇s semantic texton forests （STFs） method. Second, we also propose a new HSLT model in terms of the sparse regional semantic information to automatically build a semantic image hierarchy, which explicitly encodes a general to specific image relationship. And last, we archived image dataset using image hierarchical semantic, which is help to improve the performance of image organizing and browsing. Extension experimefital results showed that the use of semantic hierarchies as a hierarchical organizing frame- work provides a better image annotation and organization, improves the accuracy and reduces human＇s effort.     

基于局部DCT系数的图像压缩感知编码与重构

引入了压缩感知(Compressed sensing, CS)理论, 给出了在获取局部二维 离散余弦变换(Discrete cosine transform, DCT)系数的基础上高质量地编码与重构图像的新方法. 研究了在无量化和有量化情况下, 基于局部DCT系数的图像CS最小全变差重构算法. 在对DCT系数进行量化的过程中得到含噪的局部DCT系数, 在此基础上设计了能完成CS重构的图像编解码一般流程, 并构建了实际应用系统. 实验结果表明, 对于稀疏性较强的图像, 在图像编解码系统中结合CS理论与方法能得到高质量的重构图像, 与传统的直接反离散余弦变换(Inverse DCT, IDCT)方法相比, 峰值信噪比(Peak signal to noise ratio, PSNR)最大能提高5dB以上, 对于一般图像, PSNR也有较大提高.     

基于压缩域的图象检索技术研究进展

压缩标准的出现 ,使得图象数据格式普遍为压缩格式 ,从而促进了压缩域内图象检索技术的迅速发展 .为了使人们对基于压缩域的图象检索技术有一概括了解 ,该文对目前的压缩域检索技术进行了回顾和讨论 :首先 ,介绍了图象检索系统的基本概念 ;然后 ,分类分析了不同压缩域的检索技术 ,包括变换域方法 (如傅立叶变换、离散余弦变换 (DCT)以及子带和小波变换 )和空域方法 (如矢量量化和分形等 ) ;接着 ,对这些检索方法进行了讨论和比较 ,并得出一些有用的结论 ;还举例介绍了基于压缩域图象检索技术的实际应用 ;最后对压缩域图象检索技术的研究发展及其应用前景指出了一些可能的方向 .     

基于离散分数余弦变换的图像加密算法研究*

为了提高图像加密的效率和安全性,采用态函数线性组合法构造了一种离散分数余弦变换函数,利用其良好的正交性能及具有分数阶参数和周期参数的特点,提出了一种基于离散分数余弦变换的图像加密新方法。该加密算法采用了图像分块（图元）的方法。将图像分成大小不同的图元,使用离散分数余弦变换对每个图元分别进行行变换和列变换,从而完成对图像的加密。实验结果分析表明,该算法的加密效果、效率和抗穷举攻击能力是令人满意的。     

结合二维Logistic映射与二维离散余弦变换的数字图像隐藏技术

为了应对日益增大的图像安全问题, 提出一种结合二维Logistic映射与二维离散余弦变换(2D-DCT)的数字图像隐藏算法. 首先, 利用二维Logistic映射产生的混沌序列对秘密图像进行像素扩散与置乱, 从而达到对秘密图像的加密效果. 接着, 分块对载体图像进行二维离散余弦变换, 然后把扩散与置乱后的图像信息分存在变换后每块的右下角. 最后再进行二维离散余弦反变换(2D-IDCT), 并得到隐密图像. 实验结果也表明, 该算法安全可行且隐藏效果良好.     

A survey on distributed compressed sensing： theory and applications

The compressed sensing （CS） theory makes sample rate relate to signal structure and content. CS samples and compresses the signal with far below Nyquist sampling frequency simultaneously. However, CS only considers the intra-signal correlations, without taking the correlations of the multi-signals into account. Distributed compressed sensing （DCS） is an extension of CS that takes advantage of both the inter- and intra-signal correlations, which is wildly used as a powerful method for the multi-signals sensing and compression in many fields. In this paper, the characteristics and related works of DCS are reviewed. The framework of DCS is introduced. As DCS＇s main portions, sparse representation, measurement matrix selection, and joint reconstruction are classified and summarized. The applications of DCS are also categorized and discussed. Finally, the conclusion remarks and the further research works are provided.     

Liver Segmentation Technique for CT Images Based on Statistical Analysis

This article presented the automatic diagnosis of liver pathologies and its 3D volume rendering. The first and fundamental step in all these studies is the automatic liver segmentation, which is still an open problem. In this thesis, two automatic methods are described to segment the liver from abdominal CT image data. The first step is image enhancement. Secondly, texture analysis is using standard statistical measures, finally it is a rough mask to segment the liver.     

Cropping and Noise Resilient Steganography Algorithm Using Secret Image Sharing

This paper proposes an image steganography scheme, in which a secret image is hidden into a cover image using a SIS （secret image sharing） scheme. Taking advantage of the fault tolerance property of the （k, n）-threshold SIS, where using any k of n shares （k ≤ n）, the secret data can be recovered without any ambiguity. In order to increase the security of the secret information to digital attacks, the proposed steganography algorithm becomes resilient to cropping and impulsive noise contamination using SIS scheme. Among many SIS schemes proposed until now, the Lin and Chan＇s scheme is selected as the SIS, due to its lossless recovery capability of a large amount of secret data. Stego-image quality and hiding capacity depend on the prim number used in polynomial. The proposed scheme is evaluated from several points of view, such as imperceptibility of the stego-image respect to its original cover image using the PSNR, quality of the extracted secret image, robustness of hidden data to cropping operation, impulsive noise contamination and the combination of both attacks. The evaluation results show a high quality of the extracted secret image from the stego-image when it suffered more than 20% cropping and/or high density noise contamination.     

A theoretical framework for quantum image representation and data loading scheme

Two fundamental problems exist in the use of quantum computation to process an image or signal.The first one is how to represent giant data,such as image data,using quantum state without losing information.The second one is how to load a colossal volume of data into the quantum registers of a quantum CPU from classical electronic memory.Researches on these two questions are rarely reported.Here an entangled state is used to represent an image(or vector)for which two entangled registers are used to store a vector component and its classical address.Using the representation,n1+n2+8 qubits are used to store the whole information of the gray image that has a 2n1×2n2 size at a superposition of states,a feat is not possible with a classic computer.The way of designing a unitary operation to load data,such as a vector(or image),into the quantum registers of a quantum CPU from electronic memory is defined herein as a quantum loading scheme(QLS).In this paper,the QLS with time complexity O(log2N)is presented where N denotes the number of vector components,a solution that would break through the efciency bottleneck of loading data.QLS would enable a quantum CPU to be compatible with electronic memory and make possible quantum image compression and quantum signal processing that has classical input and output.     

Use of quotient-embedding scheme with smart arrangement technique to hide gray-scale data

A DCT-based digital watermarking by using quotient-embedding scheme with smart arrangement technique is proposed. To increase the hidden capacity while maintaining the robustness of the proposed method, input data is bundled to a set of 4-octet with a smart arrangement and embedded in the predetermined low- and middle-frequency coefficients of a DCT block. Simulations show that the mixed images generated by the proposed method are tolerant with image processing operations such as JPEG, JPEG2000, cropping, blurred, sharpening, low-pass filtering, noise additions, brightness, posterize, （color） quantization, and inverting. Moreover, the average PSNR for the proposed method is 40.35 dB with an embedding rate of 0.50 （bpp） white the resultant perceived quality is good.     

Study of the Preprocessing Influence in the Accuracy of Semi-automated Shadow Detection Approach

Shadows may occupy a significant portion of the image mainly in urban scenes. This research has the objective to detect shadows in high resolution orbital images using morphological operators. In order to verify preprocessing contribution in this shadow detection methodology, we have tested the median, morphological, bilateral and mean curvature filters to evaluate which one has the characteristic of mitigate the noise of the images and contribute to enhance detection performance. During the study, 10 panchromatic images of Worldview II satellite from the urban area of Presidente Prudente, in the state of Sao Paulo, were used. According to the shadow detection methodology by mathematical morphology, we checked the accuracy value using the images resulting of the smoothing methods applied in the preprocessing step. Finally, we evaluated all the smoothing levels in order to select the most appropriated according to accuracy and if images preserves the elements of interest. By analyzing the obtained results it is easy to see that the bilateral filter has presented satisfactory results, since it considers the spatial domain in the smooth ing process, despite incorporating the pixels intensity domain as well. Therefore, we can conclude that the bilateral filter is a good alternative considering an adequate choice of the parameters.     

Spatio-Temporal Video Motion Detection Using Marked-Watershed and Morphological Operations

In this article, we describe a moving object detection method developed based on spatio-temporal information and marked-watershed for extracting the moving objects from a video sequence. The algorithm begins with difference image between two adjacent frames and, using the Canny operator on the difference image, determines the initial edge mask for the object in motion Morphological operators are applied to the initial edge map to obtain a temporal segmentation mask of the moving object and binary marker image of the foreground and background, which is subject to the watershed thresholding. The markers are used to modify multi-scale morphological gradient image of the current frame. Finally, the watershed algorithm is performed on the modified gradients to locate the non-stationary objects accurately in the spatial domain of motion frames. Experimental results show that the proposed technique can overcome the shortcoming of over-segmentation of the watershed algorithm. In computationally efficient way, it segments and extracts semantically meaningful objects, which are in slow or fast motion from the video frames of scenes involving complex background. Performance evaluation yields that the detection accuracy can be as high as 98% to 99% for different video sequences.     

A novel binary image representation algorithm by using NAM and coordinate encoding procedure and its application to area calculation

We propose a novel binary image representation algorithm using the non-symmetry and anti-packing model and the coordinate encoding procedure （NAMCEP）. By tak- ing some idiomatic standard binary images in the field of image processing as typical test objects, and by comparing our proposed NAMCEP representation with linear quadtree （LQT）, binary tree （Bintree）, non-symmetry and anti-packing model （NAM） with K-lines （NAMK）, and NAM representa- tions, we show that NAMCEP can not only reduce the aver- age node, but also simultaneously improve the average com- pression. We also present a novel NAMCEP-based algorithm for area calculation and show experimentally that our algo- rithm offers significant improvements.     

基于改进型正交有限脊波分析的自适应图像去噪

提出了一种改进型正交有限脊波分析的图像去噪算法。正交有限脊波分析处理信息时,有限域平面上的直线由于模运算的存在而引起信息Radon系数的卷绕,为此,对Radon系数周期振荡方向的信息采用离散余弦变换处理,其他方向信息采用小波分析处理,以减少卷绕对信息重建影响;同时,结合图像信息中噪声的特点,提出了一种去噪浮动阈值的选取方案。仿真结果表明,采用该算法实现的图像去噪,在抑制噪声的同时较有效地保留了信号的细节,去噪图像的PSNR值较OFRIT+Wiener、2D-DWT等算法有所增加。     

SWVFS： a saliency weighted visual feature similarity metric for image quality assessment

In this paper, a saliency weighted visual feature similarity （SWVFS） metric is proposed for full reference im- age quality assessment （IQA）. Instead of traditional spatial pooling strategies, a visual saliency-based approach is em- ployed for better compliance with properties of the human visual system, where the saliency allocation is closely related to the activity of posterior parietal cortex and the pluvial nu- clei of the thalamus. Assuming that the saliency map actually represents the contribution of locally computed visual distor- tions to the overall image quality, the gradient similarity and the textural congruency are merged into the final image qual- ity indicator. The gradient and texture comparison play com- plementary roles in characterizing the local image distortion. Extensive experiments conducted on seven publicly available image databases show that the performance of SWVFS is competitive with the state-of-the-art IQA algorithms.     

A Compressed Sensing Based DV-Hop Location Algorithm for Wireless Sensor Networks

A compressed sensing based DV-hop location algorithm is presented to improve the performance of the conventional DV-hop location algorithm in WSNs （wireless sensor networks）. The sensor network can be divided into multiple grids. Compared with the grid number, the number of targets in the network is generally sparse. Therefore, the localization of the targets in the network can be transformed into a sparse signal reconstruction issue. Theoretical analysis and experimental results on the proposed algorithm show that it is able to greatly reduce the amount of data flow in the network, balance the load of communication, prolong the lifetime of the WSNs, and improve the target location accuracy, compared to the DV-hop location ones.     

Super-Resolution image Reconstruction Based on Iteration and Wavelet Transform

In this paper, we provide a super-resolution image reconstruction algorithm based on wavelet transform. Wavelet transform can separate high frequency and low frequency information of image. The more high frequency information can be obtained by using wavelet transform and the technique of image fusion. Meanwhile, reconstructed super-resolution image is produced by the iterative method. In iteration process, noise of image can be suppressed by applying method of wavelet threshold de-noising. The experiment results show that the algorithm can overcome the disadvantage of the classical interpolation method and effectively improve the resolution and PSNR of the image.