基于ZedBoard的超分辨率视频复原系统

为了实现超分辨率视频图像的实时复原,设计了以ZedBoard可编程片上系统为基础的超分辨率视频复原系统。系统包括基于V4L2(Video for Linux 2)的USB摄像头视频采集、基于小波变换的超分辨率复原算法处理和基于Qt的图形用户界面制作以及视频输出。采用双线性、双立方和小波变换算法分别对Lena图像进行复原处理,峰值信噪比PSNR值分别为29.516、29.843、31.368。实验结果表明,提出的基于小波的超分辨率复原算法优于传统的插值算法,基于ZedBoard的超分辨视频复原系统复原效果良好。     

基于小波变换的深度预测复原算法研究

提出了一种基于小波变换的深度预测算法,并将此算法应用于计算光学切片的图像复原.对序列图进行一层小波分解,然后确定阈值和预测规则,对序列图进行预测.根据预测结果进行小波反变换,将图像复原.实验表明:小波系数应取三个高频分量的绝对值及部分低频信息之和;阈值Г随添加低频分量值的比例的增大而增大;采用微分算子规则具有较好的预测和复原效果.该方法计算量小、不需要迭代、不需要点扩展函数,针对没有交叠的序列图有较好的复原效果.     

Multiscale top-hat selection transform based infrared and visual image fusion with emphasis on extracting regions of interest

To effectively combine regions of interest in original infrared and visual images, an adaptively weighted infrared and visual image fusion algorithm is developed based on the multiscale top-hat selection transform. First, the multiscale top-hat selection transform using multiscale structuring elements with increasing sizes is discussed. Second, the image regions of the original infrared and visual images at each scale are extracted by using the multiscale top-hat selection transform. Third, the final fusion regions are constructed from the extracted multiscale image regions. Finally, the final fusion regions are combined into a base image calculated from the original images to form the final fusion result. The combination of the final fusion regions uses the adaptive weight strategy, and the weights are adaptively obtained based on the importance of the extracted features. In the paper, we compare seven image fusion methods: wavelet pyramid algorithm (WP), shift invariant discrete wavelet transform algorithm (SIDWT), Laplacian pyramid algorithm (LP), morphological pyramid algorithm (MP), multiscale morphology based algorithm (MSM), center-surround top-hat transform based algorithm (CSTHT), and the proposed multiscale top-hat selection transform based algorithm. These seven methods are compared over five different publicly available image sets using three metrics of spatial frequency, mean gradient, and Q. The results show that the proposed algorithm is effective and may be useful for the applications related to the infrared and visual image fusion.     

基于Contourlet变换的迭代图像复原算法

考虑到contourlet变换的多尺度多方向性以及对二维图像具有比小波变换更好的稀疏表示特性,提出了一种基于contourlet变换的图像复原算法.算法采用边界优化的方法,通过类期望最大化算法在contourlet域进行迭代计算,并最终获得惩罚似然函数的最优解.实验结果表明.与传统的基于小波变换的同类图像复原算法相比,基于contourlet变换的复原算法在保持了较低的运算代价的同时,更好地保护了图像的边缘和细节信息,峰值信噪比有0.6 dB～0.8 dB左右的提高.     

Multiple-image encryption by two-step phase-shifting interferometry and spatial multiplexing of smooth compressed signal

A multiple-image encryption method based on two-step phase-shifting interferometry(PSI) and spatial multiplexing of a smooth compressed signal is proposed. In the encoding and encryption process, with the help of four index matrices to store original pixel positions, all the pixels of four secret images are firstly reordered in an ascending order; then, the four reordered images are transformed by five-order Haar wavelet transform and performed sparseness operation. After Arnold transform and pixels sampling operation, one combined image can be grouped with the aid of compressive sensing(CS)and spatial multiplexing techniques. Finally, putting the combined image at the input plane of the PSI encryption scheme,only two interferograms ciphertexts can be obtained. During the decoding and decryption, utilizing all the secret key groups and index matrices keys, all the original secret images can be successfully decrypted by a wave-front retrieval algorithm of two-step PSI, spatial de-multiplexing, inverse Arnold transform, inverse discrete wavelet transform, and pixels reordering operation.     

一种基于非下采样Contourlet变换多聚焦图像融合算法

针对现有小波类图像融合算法的不足,提出了一种基于非下采样Contourlet变换多聚焦图像融合算法,并在Contourlet域中引入了局部区域可见度以及局部方向能量的概念.针对低频子带系数和各带通方向子带系数分别提出了基于局部区域可见度以及基于局部方向能量的系数选择方案.通过对多聚焦图像融合的仿真实验,表明该算法相对于传统的基于离散小波变换和离散小波框架变换融合算法能够有效减少有用信息的丢失以及虚假信息的引入,同时能够从源图像中提取更多的有用信息并注入到融合图像中, 得到更好视觉效果和更优量化指标的融合图像.     

基于分块DCT变换编码的小波域多幅图像融合算法

提出了一种利用DCT变换和小波变换的特征层图像融合算法.其基本思想是先对多幅源图像进行分块DCT变换,选取较大方差对应的变换系数,将图像压缩为原图像大小的1/4,保留系数的对应坐标作为提取信息时的密钥;其次将经处理后的DCT系数直接作为小波变换的分解系数,经小波逆变换后得到融合信息.实验结果表明,该算法实现了多幅不同大小图像的融合,同时单一密钥只能提取单一图像. 关键词： 图像融合 小波变换 离散余弦变换 编码     

Multimodality Image Fusion Based on Quantum Wavelet Transform and Sum-Modified-Laplacian Rule

The parallelism and entanglement characteristics of quantum computation greatly improve the efficiency of image processing tasks. With the sharp increase of data size and requirement of real-time processing in image fusion application, rapid implementation using quantum computation will become the inexorable trend. A novel multimodality image fusion algorithm based on quantum wavelet transform (QWT) and proposed quantum version of sum-modified-laplacian (SML) rule is designed in this paper. The source digital images are firstly represented by flexible representation of quantum image (FRQI) model, and then the quantum form images are transformed with QWT to capture salient features of source images. The quantum version of SML rule is proposed to fuse wavelet coefficients, which has higher efficiency and runs faster than its classical counterpart. The final fused image is obtained by using inverse quantum wavelet transform. The simulations and theoretical analysis verify that the proposed algorithm is effective in the fusion of multimodality images.

     

基于可见光的多波段偏振图像融合新算法

采用了一种新的基于小波变换的偏振图像融合算法.首先,将两个波段中的每一波段三幅偏振图像利用小波变换分解成低频和高频部分,低频的小波系数平均值作为融合后的低频系数,高频细节系数根据不同区域特征选择方法以及对应输入图像小波系数的窗口区域方差来确定融合后高频小波系数,得到一个波段一幅图像.接着,将得到的图像再进行小波分解,采用低频图像的小波系数最小值作为融合后的低频系数,高频图像根据纹理一致性测度的纹理检测确定融合规则,用来调整高频小波系数,将来自不同图像的特征与细节融合在一起,并对融合图像质量进行了对比评价.实验结果表明,融合后的偏振图像不仅反映了场景的偏振信息,而且还包含了丰富的光谱信息,目标与背景的衬比度也得到了增强,为进一步的目标检测和识别提供了便利.     

结合空化微泡母小波变换的高空化噪声比超快速 主动空化成像方法*

本文提出一种结合空化微泡母小波技术(Cavitation Bubble Wavelet Transform, CBWT)、波束合成和平方差值求和减影(Sum-of-Squared Differences, SSD)的超快速主动空化成像方法。该方法首先发射平面波并接收空化回波信号;其次,基于RPNNP模型构建空化微泡母小波,对空化回波信号进行连续小波变换;再次,对获得的小波系数进行波束合成,包括延迟叠加算法(Delay-and-Sum, DAS)、最小方差算法(Minimum-Variance, MV)和相干系数最小方差算法(Coherence-Factor-based-Minimum-Variance, MVCF),再结合SSD,得到空化图像。结果表明,与未采用CBWT相比,基于CBWT-DAS-SSD、CBWT-MV-SSD和CBWT-MVCF-SSD的空化噪声比分别提高了16.34 dB、15.07 dB、17.71 dB。本文方法可提高空化成像质量,为空化动态实时监控提供参考。     

二维提升的CDF(1,3)小波结合改进的SPIHT的渐进性无损图像压缩方法

针对传统小波变换计算复杂的缺点和多级树集合分裂算法(SPIHT)编码过程重复运算、存储量大的问题,提出了一种二维提升的CDF(1,3)小波结合改进的SPIHT的渐进性无损图像压缩方法。对整数CDF(1,3)双正交小波变换实现二维提升,利用提升的小波对图像做变换,提高了运算速度、便于硬件实现。对SPIHT算法加以改进,根据各个子图像的不同特点,改变扫描路线,采用四路并行分块处理的方法,提高了编码速度,降低了编解码过程的运算复杂度和时间消耗。利用提升的CDF(1,3)小波变换结合改进的SPIHT实现了渐进性无损图像压缩,证明了二维提升方案的有效性。     

一种基于提升小波的图像融合技术研究

针对红外与可见光图像进行提升小波变换后低频图像的特点,提出了一种低频分量的融合算法,高频分量采取邻域方差取大为准则进行融合,然后进行提升小波逆变换得到融合图像。通过与传统小波融合方法进行比较,并引入信息熵、清晰度、Xydeas-Petrovic客观性能指标对融合后的图像进行分析。实验结果表明不论从视觉效果还是从客观性能指标上,该算法都优于传统的融合方法。     

干涉多光谱卫星图像序列编码

星载干涉多光谱图像序列通过图像匹配实行定位形成光谱序列,相邻图像之间具有很强的相关性,与一般图像序列不同的是,相邻图像之间具有明显的平移特点。为了充分利用这一特点实现有效的图像压缩,同时减少编码系统复杂度,提出了一种新的图像序列编码方法,通过小波域系数匹配算法检测出相邻图像之间的相对位移量,然后对差值图像进行类似于单幅图像的编码,从而提高了总体编码效率。本算法具有与相同单幅图像编码算法相当的低复杂度特点,只需要对单幅图像与模板的差值进行基于小波变换的编码,从而避免了基于三维小波变换的编码算法对系统存储量要求大以及编码延时大的缺陷。仿真结果表明,本算法比基于三维小波变换的编码算法效果更好。     

Remote-sensing image encryption in hybrid domains

Remote-sensing technology plays an important role in military and industrial fields. Remote-sensing image is the main means of acquiring information from satellites, which always contain some confidential information. To securely transmit and store remote-sensing images, we propose a new image encryption algorithm in hybrid domains. This algorithm makes full use of the advantages of image encryption in both spatial domain and transform domain. First, the low-pass subband coefficients of image DWT (discrete wavelet transform) decomposition are sorted by a PWLCM system in transform domain. Second, the image after IDWT (inverse discrete wavelet transform) reconstruction is diffused with 2D (two-dimensional) Logistic map and XOR operation in spatial domain. The experiment results and algorithm analyses show that the new algorithm possesses a large key space and can resist brute-force, statistical and differential attacks. Meanwhile, the proposed algorithm has the desirable encryption efficiency to satisfy requirements in practice.     

A novel multi-scale segmentation algorithm for high resolution remote sensing images based on wavelet transform and improved JSEG algorithm

Considering the difficulties in image segmentation caused by the complexity of diverse ecological environments and various artificial targets in high resolution remote sensing images, especially in city scene, and in order to overcome the limitations existing in the traditional segmentation algorithm, JSEG (J-Segmentation), for high resolution remote sensing image segmentation and to further improve the segmentation accuracy, WJSEG (Wavelet-JSEG), a novel multi-scale segmentation algorithm based on wavelet transform, is proposed, which is an improved JSEG algorithm. WJSEG is an improved form of JSEG in relation to three aspects, including color quantization, multi-scale segmentation and region merging by introducing the multi-scale analysis tool based on wavelet transform. Experiments have been conducted on high resolution SPOT 5 pan-sharpened multispectral image and IKONOS panchromatic image. These experimental results were compared with those gained by the traditional JSEG algorithm and the famous commercial software named eCognition, which validated the effectiveness and reliability of the proposed WJSEG algorithm.     

Adaptive image enhancement based on clustering of wavelet coefficients for infrared sea surveillance systems

Most of the techniques developed for infrared (IR) image enhancement (IE) depend heavily on the scene, environmental conditions, and the properties of the imaging system. So, with a set of predefined scenario properties, a content-based IR-IE technique can be developed for better situational awareness. This study proposes an adaptive IR-IE technique based on clustering of wavelet coefficients of an image for sea surveillance systems. Discrete wavelet transform (DWT) of an image is computed and feature vectors are constructed from subband images. Clustering operation is applied to group similar feature vectors that belong to different scene components such as target or background. Depending on the feature vectors, a weight is assigned to each cluster and these weights are used to compute gain matrices which are used to multiply wavelet coefficients for the enhancement of the original image. Enhancement results are presented and a comparison of the performance of the proposed algorithm is given through subjective tests with other well known frequency and histogram based enhancement techniques. The proposed algorithm outperforms previous ones in the truthfulness, detail visibility of the target, artificiality, and total quality criteria, while providing an acceptable computational load.     

Multi-sensor image fusion using discrete wavelet frame transform

An algorithm is presented for multi-sensor image fusion using discrete wavelet frame transform (DWFT).The source images to be fused are firstly decomposed by DWFT. The fusion process is the combining of the source coefficients. Before the image fusion process, image segmentation is performed on each source image in order to obtain the region representation of each source image. For each source image, the salience of each region in its region representation is calculated. By overlapping all these region representations of all the source images, we produce a shared region representation to label all the input images. The fusion process is guided by these region representations. Region match measure of the source images is calculated for each region in the shared region representation. When fusing the similar regions, weighted averaging mode is performed; otherwise selection mode is performed. Experimental results using real data show that the proposed algorithm outperforms the traditional pyramid transform based or discrete wavelet transform (DWT) based algorithms in multi-sensor image fusion.     

一种基于小波变换的红外偏振融合算法

针对红外偏振图像可以较好地抑制背景噪声,对目标边缘信息比较敏感的特点,提出一种基于小波变换的红外偏振融合算法,它主要用于红外辐射强度图像和偏振度图像融合,增加图像的信息量。首先采用小波变换对参与融合的每幅图像分别进行各尺度分解,得到各尺度小波系数,然后针对不同尺度小波系数,采用邻域平均梯度为判据进行融合,得到融合后的各尺度小波系数,最后通过小波逆变换进行图像重构,得到融合图像。融合前后的图像对比表明融合图像在保留辐射强度图像的清晰度的同时,突出了目标的边缘、轮廓信息。相对于辐射强度图像,融合图像的梯度均值提高了112%,相对于偏振度图像,融合图像的标准差提高了151%,信息熵提高了38%。     

Quantum Multi-Image Encryption Based on Iteration Arnold Transform with Parameters and Image Correlation Decomposition

A novel quantum multi-image encryption algorithm based on iteration Arnold transform with parameters and image correlation decomposition is proposed, and a quantum realization of the iteration Arnold transform with parameters is designed. The corresponding low frequency images are obtained by performing 2-D discrete wavelet transform on each image respectively, and then the corresponding low frequency images are spliced randomly to one image. The new image is scrambled by the iteration Arnold transform with parameters, and the gray-level information of the scrambled image is encoded by quantum image correlation decomposition. For the encryption algorithm, the keys are iterative times, added parameters, classical binary and orthonormal basis states. The key space, the security and the computational complexity are analyzed, and all of the analyses show that the proposed encryption algorithm could encrypt multiple images simultaneously with lower computational complexity compared with its classical counterparts.     

结合小波域变换和空间域变换的图像增强方法

提出了一种结合了小波域和空间域处理方法的图像增强算法. 该算法首先对小波域中的高频系数进行修正,使图像具有更好的局部对比度和更丰富的细节,由于双树复小波变换（Dual-tree Complex Wavelet Transform,DT CWT）具有更好的方向选择性,在小波变换的过程中选用了这一方法;然后,通过空间域中的非线性变换,调整图像的整体对比度. 该算法可根据图像本身的特性实现参数的自动选择. 经过本文方法的处理所得的图像,无论在视觉效果上还是在统计上,都优于前人工作的结果.