一种基于边缘方向的双线性插值方法

针对传统的双线性插值存在的图像边缘模糊问题,本文提出一种改进方法。首先根据插值点的几何位置及邻点的灰度大小,确定插值点灰度的梯度方向和边缘方向,然后沿边缘方向进行双线性插值。该方法可实现沿任意边缘方向的插值,并且计算简单。实验结果表明,该方法与传统的双线性插值法相比,插值图像的平均梯度增加而均方误差减小,有效地提高了插值图像的质量。     

Reversible data hiding based on RSBEMD coding and adaptive multi-segment left and right histogram shifting

By leveraging the secret data coding using the remainder storage based exploiting modification direction (RSBEMD), and the pixel change operation recording based on multi-segment left and right histogram shifting, a novel reversible data hiding (RHD) scheme is proposed in this paper. The secret data are first encoded by some specific pixel change operations to the pixels in groups. After that, multi-segment left and right histogram shifting based on threshold manipulation is implemented for recording the pixel change operations. Furthermore, a multiple embedding policy based on chess board prediction (CBP) and threshold manipulation is put forward, and the threshold can be adjusted to achieve adaptive data hiding. Experimental results and analysis show that it is reversible and can achieve good performance in capacity and imperceptibility compared with the existing methods.     

基于单向差值扩展的彩色图像可逆数据隐藏

针对Tian差值扩展算法存在过分修改像素值、须嵌入定位图等缺点,提出一种基于单向差值扩展的彩色图像可逆数据隐藏算法。利用色彩分量间的相关性减小差值,并用较小的差值扩展量扩展单个色彩分量的像素值,扩展方向由上溢和下溢像素的数量决定;采用预先调整像素值的方法避免像素溢出,少量的调整信息即可代替定位图,嵌入容量大幅提高;提取端根据临界像素值的顺序定位调整像素的位置,用调整信息恢复调整像素,因此不需要嵌入定位图,在提取信息的同时可无损地恢复原始图像。实验结果表明,本文算法在保证图像质量的同时,嵌入容量有很大的提高。     

Improved PVO-based reversible data hiding: A new implementation based on multiple histograms modification

Pixel-value-ordering (PVO) technique is based on ranking the pixels within a block and often employed in reversible data hiding (RDH) to preserve the image fidelity. In this paper, an improved PVO-based algorithm is proposed to achieve a high-performance RDH. We formulate the PVO embedding as a problem of multiple histograms modification (MHM) and make two contributions. Firstly, the existing PVO-based algorithms are generalized in an MHM form and the systematic analysis of performance is offered by considering the parameter determination. Secondly, based on the new framework, an improved implementation of PVO embedding is proposed to adaptively determine the MHM manner by solving the distortion minimization of multiple histograms, which can handle different characteristics in image areas. Experimental results show the superiority of the new algorithm over the state-of-the-art methods.     

基于排序和直方图修改的可逆信息隐藏方法

提出一种基于排序的差值直方图修改的可逆信息隐藏方法。首先,对图像分块;然后,采用按灰度值大小排序后的中值作为参考点,并和其余像素点做差,以产生更多的相同的值,使其差值直方图更紧凑、直方图的峰值点更大;最后,采用基于直方图修改的方法在差值中进行隐藏。实验表明,该方法不仅可以实现可逆信息隐藏,隐藏效果更好,并且嵌入量更高。为了提高嵌入量,进行了多层信息嵌入,并且在嵌入层数不同时采用不同的分块方式,提高了隐藏效果。分析了实验结果。     

A novel high-performance reversible data hiding scheme using SMVQ and improved locally adaptive coding method

Reversible data hiding is a method that not only embeds secret data but also reconstructs the original cover image without distortion after the confidential data are extracted. In this paper, we propose novel reversible data hiding scheme that can embed high capacity of secret bits and recover image after data extraction. Our proposed scheme depends on the locally adaptive coding scheme (LAC) as Chang&Nguyen’s scheme and SMVQ scheme. Experimental results show that the compression rate of our proposed scheme is 0.33 bpp on average. To embed secret bits we propose the normal-hiding scheme and the over-hiding scheme which have an average embedding rate of 2.01 bpi and 3.01 bpi, more than that of Chang&Nguyen’s scheme (1.36 bpi). The normal-hiding scheme and the over-hiding scheme also has high embedding efficiency of 0.28 and 0.36 on average, which are better than that of Chang&Kieu’s scheme (0.12), Chang&Nguyen’s scheme (0.18) and Chang&Nguyen’s scheme (0.16).     

基于耦合映象格子的图像分割方法

为了实现图像的自适应多级分割,利用混沌动力学系统对初值的敏感性和全局耦合映象混沌同步的特点,提出了一种基于耦合映象格子的图像分割方法.方法对每个灰度等级构建全局耦合映象,建立灰度等级间关系矩阵和灰度等级更新模型,通过全局耦合映象混沌同步过程完成灰度等级的自适应聚类.实验结果表明方法对中间类灰度等级聚类具有很好的鲁棒性,分割过程不需要人工干预,并且具有较强的灵活性,优于阈值分割和边缘检测的图像分割方法.     

一种基于激光测距的机器人地图创建方法

针对未知环境中机器人局部地图的构建问题,提出一种激光测距的离散数据自适应曲率估计算法。采用该算法对预处理后的激光扫描数据进行环境特征分割,然后提取并描述环境特征,最后根据特征元素集构建局部地图。仿真和实验结果表明,该方法能准确地描述结构化室内环境,有效地描述和表征了实验环境。     

Reversible data embedding for indices based on histogram analysis

Reversible data hiding is a technique that not only protects the hidden secrets but also recovers the cover media without any distortion after the secret data have been extracted. In this paper, a new reversible data hiding technique for VQ indices which are compressed streams based on the mapping function and histogram analysis of transformed VQ indices is introduced to enhance the performance of some earlier reversible data hiding schemes that are based on VQ indices. As a result, the proposed scheme achieves high embedding capacity and data compression simultaneously. Moreover, the original VQ-compressed image can be perfectly reconstructed after secret data extraction. To estimate the performance of the proposed scheme, variety of test images are used in the experimental testing. As can be seen in the experimental result, our scheme is superior to some previous schemes in term of compression rate and embedding rate while maintaining the reversibility.     

一种基于相位差拟合的二次相关测向处理方法

相位差拟合测向处理方法较之传统的相关干涉仪测向方法具有更高的精度,但存在计算量过大的缺点.针对这一问题,提出了一种基于相位差拟合的二次相关测向处理方法.该方法通过粗测找到来波方向范围,在该范围内进行相位差拟合,生成拟合标准库,并在此基础上进行第二次相关.经仿真验证,该方法在保证测向精度的同时,极大地提高了系统的测向速度.     

A reversible steganographic scheme for VQ indices based on locally adaptive coding

Achieving a high embedding capacity and low compression rate with a reversible data hiding method in the vector quantization (VQ) compressed domain is a technically challenging problem. This paper proposes a novel reversible steganographic scheme for VQ compressed images based on a locally adaptive data compression method. The proposed method embeds n secret bits into one VQ index of an index table in Hilbert-curve scan order. The experimental results show that the proposed method can achieve the different average embedding rates of 0.99, 1.68, 2.28, and 3.04 bit per index (bpi) and average compression rates of 0.45, 0.46, 0.5, and 0.56 bit per pixel (bpp) for n = 1, 2, 3, and 4, respectively. These results indicate that the proposed scheme is superior to Chang et al.’s scheme 1 [19], Yang and Lin’s scheme [21], and Chang et al.’s scheme 2 [24].     

基于方向余弦的交会方法

在多台光电经纬仪组成的测量网络中,为了快速得 到目标的空间位置信息,提出了一种基于方向余弦的交会方法。本文方法根据光电经纬仪实 时跟踪被测目标时的几何关系,利用光电经纬仪的站点位置和实时获得的测角信息,对每台 光电经纬仪都构建两个方向余弦,利用加权算法实时估计出被测目标的空间三维坐标。试验 结果表明,对于近距离的空间目标,与雷达的测量数据相比,本文方法得到的目标位置数据 平滑、无波动,二者的定位误差均小于0.5m,能满足目标高精度的 定位要求。     

Reversible data hiding based on multiple histograms modification and deep neural networks

In the previous multiple histograms modification (MHM) based reversible data hiding (RDH) method, the prediction-error histograms are generated by a fixed manner, which may constrain the performance owing to the lack of adaptivity. In order to compensate this, we propose a deep neural networks (DNN) based method for dynamical multiple histograms generation. Through learning the prior knowledge, DNN is able to establish the histograms with different sizes for a better redundancy exploitation. For each histogram, two optimal expansion bins will be determined to minimize the distortion caused by the modification. Besides, the strategy consisted of the memo technique and the entropy measurement are applied to accelerate the parameter optimization. Experimental results show that the proposed method outperforms some of state-of-the-art RDH methods.     

A novel steganalysis framework of heterogeneous images based on GMM clustering

The current steganalysis frameworks involve a large number of techniques for feature extraction and classification. However, one of their common defects is treating all images as equal, thus ignoring the variability of statistical properties of different images, which motivates us to propose a novel steganalysis framework based on Gaussian mixture model (GMM) clustering in the study, targeting at heterogeneous images with different texture complexity. There are two main improvements compared to the current steganalysis frameworks. First, in the training stage, the GMM clustering algorithm is exploited to classify the training samples into limited categories automatically, and then design corresponding steganalyzers for each category; second, in the testing stage, the posterior probability of testing samples belonging to each category is calculated, and the samples are submitted to the steganalyzers corresponding to the maximum posterior probability for test. Extensive experimental results aiming at least significant bit matching (LSBM) steganography and two adaptive steganography algorithms show that the proposed framework outperforms the steganalysis system that is directly trained on a mixed dataset, and also indicate that our framework exhibits better detection performance compared to the representative framework for using image contents in most circumstances and similar detection performance in few cases.     

Reversible data hiding based on multilevel histogram modification and pixel value grouping

This paper proposes a multilevel histogram modification based reversible data hiding scheme using a new difference generation strategy called pixel value grouping (PVG). It aims to produce shaper difference histogram by exploiting the high correlation among pixels within block. After sorting, pixel values are grouped according to their distribution. For each set of similar pixel values, real or virtual reference pixel will be determined to compute differences in the scope of pixel values group and next secret message is embedded through expansion embedding. By PVG, we success to greatly reduce the number of to-be-shifted pixels while producing sufficient EC and hence less distortion can be introduced for embedding the same payload. Moreover, the same grouping can be achieved at the decoder and the real or virtual reference pixel can be determined without any prior knowledge, which guarantees the reversibility. Experimental results demonstrate that our scheme outperforms previous related state-of-the-art schemes.     

基于一种特定混沌映射的跳频序列

使用一种简单的分段线性映射构造混沌跳频序列。用于跳频通信中取代传统的伪随机序列（PN序列），分析了此混沌序列的周期性。平衡性，汉明相关特性和线性间隔，最后给出了混沌序列的神经网络实现。仿真和实验结果表明，此序列具有较好的统计特性。应用于跳频多址通信中，可获得较好的抗干扰。抗截获能力。     

A novel multi-focus image fusion method based on distributed compressed sensing

Multi-focus image fusion aims to produce an all-in-focus image by merging multiple partially focused images of the same scene. The main work is identifying the focused region and then composing all the focused regions. In this paper, a novel efficient multi-focus image fusion method based on distributed compressed sensing (DCS) is proposed. Firstly, the low-frequency and high-frequency images are obtained by comparing the variance of the source images, which are further utilized to get the low-frequency and high-frequency dictionaries. Secondly, DCS using joint sparsity model-1 (JSM-1) is applied to reconstruct the precise high-frequency images. Thirdly, the decision map is obtained based on all the high-frequency images and then improved by the morphological processing. Finally, the focused pixels are chosen from the source images through the decision map. Experimental results indicate that the proposed DCS-based method can be competitive with or even outperform some state-of-the-art methods in terms of both visual and quantitative metric evaluations.     

一种基于深度强化学习的测向基线布阵技术

针对现在工程项目中测向阵列基线的布阵方法存在耗时耗力、严重依赖于技术人员测向经验且复杂测向阵列难以设计等问题,提出了一种利用深度强化学习方法实现测向阵列基线自动生成技术。基于相关干涉仪测向机制,采用深度强化学习方法构建测向布阵智能体,重点突破多场景多实体仿真建模、布阵智能体构建、测向效能评估等关键技术;利用强化学习反复试错机理,迭代优化得到符合指标的最优测向阵列,大大提高布阵效率和测向质量,并通过实验证明了该方法的有效性。采用该技术设计的阵列基线已在实际项目中进行测向试验验证,各项指标均满足实际工程应用要求。     

基于测距与干涉式测向结合解决多值方法

为了解决安装环境有限,不能应用多天线组成的天线阵问题,达到小空间还能采用干涉式测向系统的目的,采用测距与干涉式测向相结合的方法,解决该问题。经过实际应用试验,获得较为理想应用效果,该方法具有工程实践、推广意义。     

基于分数阶矩的测向算法研究

在SαS噪声环境中基于阵列输出信号分数阶矩的TLS-ESPRIT测向算法，该算法在保持了原TLS-ESPRIT算法无需谱峰搜索，计算量和存储量小等优点的基础上，对加性SαS噪声有较好的抑制作用，扩展了ESPRIT算法的信号模型和应用环境，计算机仿真验证了该算法的何行性和有效性。