Sparsity adaptive matching pursuit(SAMP)is a greedy reconstruction algorithm for compressive sensing signals.SAMP reconstructs signals without prior information of sparsity and presents better reconstruction performan...Sparsity adaptive matching pursuit(SAMP)is a greedy reconstruction algorithm for compressive sensing signals.SAMP reconstructs signals without prior information of sparsity and presents better reconstruction performance for noisy signals compared to other greedy algorithms.However,SAMP still suffers from relatively poor reconstruction quality especially at high compression ratios.In the proposed research,the Wilkinson matrix is used as a sensing matrix to improve the reconstruction quality and to increase the compression ratio of the SAMP technique.Furthermore,the idea of block compressive sensing(BCS)is combined with the SAMP technique to improve the performance of the SAMP technique.Numerous simulations have been conducted to evaluate the proposed BCS-SAMP technique and to compare its results with those of several compressed sensing techniques.Simulation results show that the proposed BCS-SAMP technique improves the reconstruction quality by up to six decibels(d B)relative to the conventional SAMP technique.In addition,the reconstruction quality of the proposed BCS-SAMP is highly comparable to that of iterative techniques.Moreover,the computation time of the proposed BCS-SAMP is less than that of the iterative techniques,especially at lower measurement fractions.展开更多
目的为了解决图像的鲁棒性和透明性之间的矛盾,依据压缩感知理论的计算保密性提出一种基于压缩感知的强鲁棒彩色图像双水印算法。方法首先将水印图像RGB分解后,对G,B分量分块压缩感知获得测量值,再对载体图像G,B分量NSCT分解,对低频分...目的为了解决图像的鲁棒性和透明性之间的矛盾,依据压缩感知理论的计算保密性提出一种基于压缩感知的强鲁棒彩色图像双水印算法。方法首先将水印图像RGB分解后,对G,B分量分块压缩感知获得测量值,再对载体图像G,B分量NSCT分解,对低频分量非重叠分块后LU分解、奇异值分解,将每个分块的水印测量值按不同嵌入强度对应嵌入载体奇异值矩阵中,经过一系列逆变换得到含水印图像。最后用含水印图像R分量分块压缩感知测量值生成零水印,发往IPR中心注册保存。结果该算法在水印的嵌入和提取仿真实验结果中峰值信噪比大于40 d B,重建的水印图像与原图像相似度极高,且能抵抗剪切、高斯噪声、椒盐噪声、高斯低通滤波和JPEG压缩等类攻击。结论算法具有很强的鲁棒性和较好的透明性,实现较简单具有切实的可行性。展开更多
目的依据压缩感知理论具有很好的计算保密性,提出一种基于NSCT和压缩感知的数字图像水印算法,以解决图像的鲁棒性、不可感知性及保密性之间矛盾。方法首先将水印图像分块压缩感知获得测量值,然后再将载体图像NSCT分解,对低频分量Fibona...目的依据压缩感知理论具有很好的计算保密性,提出一种基于NSCT和压缩感知的数字图像水印算法,以解决图像的鲁棒性、不可感知性及保密性之间矛盾。方法首先将水印图像分块压缩感知获得测量值,然后再将载体图像NSCT分解,对低频分量Fibonacci置乱后非重叠分块,对每块进行LU分解、奇异值分解,将每个分块的水印测量值按不同的嵌入强度对应嵌入载体奇异值矩阵中,经过一系列逆变换得到含水印图像。结果该算法在水印的嵌入和提取仿真实验结果中峰值信噪比大于40 d B,重建的水印图像与原图像相似度极高,且能抵抗剪切、高斯噪声、椒盐噪声、高斯低通滤波和JPEG压缩等类的攻击。结论该算法既具有很好的鲁棒性又兼有较强的不可见性,具有切实的可行性。展开更多
To progressively provide the competitive rate-distortion performance for aerial imagery,a quantized block compressive sensing(QBCS) framework is presented,which incorporates two measurement-side control parameters:mea...To progressively provide the competitive rate-distortion performance for aerial imagery,a quantized block compressive sensing(QBCS) framework is presented,which incorporates two measurement-side control parameters:measurement subrate(S) and quantization depth(D).By learning how different parameter combinations may affect the quality-bitrate characteristics of aerial images,two parameter allocation models are derived between a bitrate budget and its appropriate parameters.Based on the corresponding allocation models,a model-guided image coding method is proposed to pre-determine the appropriate(S,D) combination for acquiring an aerial image via QBCS.The data-driven experimental results show that the proposed method can achieve near-optimal quality-bitrate performance under the QBCS framework.展开更多
文摘Sparsity adaptive matching pursuit(SAMP)is a greedy reconstruction algorithm for compressive sensing signals.SAMP reconstructs signals without prior information of sparsity and presents better reconstruction performance for noisy signals compared to other greedy algorithms.However,SAMP still suffers from relatively poor reconstruction quality especially at high compression ratios.In the proposed research,the Wilkinson matrix is used as a sensing matrix to improve the reconstruction quality and to increase the compression ratio of the SAMP technique.Furthermore,the idea of block compressive sensing(BCS)is combined with the SAMP technique to improve the performance of the SAMP technique.Numerous simulations have been conducted to evaluate the proposed BCS-SAMP technique and to compare its results with those of several compressed sensing techniques.Simulation results show that the proposed BCS-SAMP technique improves the reconstruction quality by up to six decibels(d B)relative to the conventional SAMP technique.In addition,the reconstruction quality of the proposed BCS-SAMP is highly comparable to that of iterative techniques.Moreover,the computation time of the proposed BCS-SAMP is less than that of the iterative techniques,especially at lower measurement fractions.
文摘目的为了解决图像的鲁棒性和透明性之间的矛盾,依据压缩感知理论的计算保密性提出一种基于压缩感知的强鲁棒彩色图像双水印算法。方法首先将水印图像RGB分解后,对G,B分量分块压缩感知获得测量值,再对载体图像G,B分量NSCT分解,对低频分量非重叠分块后LU分解、奇异值分解,将每个分块的水印测量值按不同嵌入强度对应嵌入载体奇异值矩阵中,经过一系列逆变换得到含水印图像。最后用含水印图像R分量分块压缩感知测量值生成零水印,发往IPR中心注册保存。结果该算法在水印的嵌入和提取仿真实验结果中峰值信噪比大于40 d B,重建的水印图像与原图像相似度极高,且能抵抗剪切、高斯噪声、椒盐噪声、高斯低通滤波和JPEG压缩等类攻击。结论算法具有很强的鲁棒性和较好的透明性,实现较简单具有切实的可行性。
文摘目的依据压缩感知理论具有很好的计算保密性,提出一种基于NSCT和压缩感知的数字图像水印算法,以解决图像的鲁棒性、不可感知性及保密性之间矛盾。方法首先将水印图像分块压缩感知获得测量值,然后再将载体图像NSCT分解,对低频分量Fibonacci置乱后非重叠分块,对每块进行LU分解、奇异值分解,将每个分块的水印测量值按不同的嵌入强度对应嵌入载体奇异值矩阵中,经过一系列逆变换得到含水印图像。结果该算法在水印的嵌入和提取仿真实验结果中峰值信噪比大于40 d B,重建的水印图像与原图像相似度极高,且能抵抗剪切、高斯噪声、椒盐噪声、高斯低通滤波和JPEG压缩等类的攻击。结论该算法既具有很好的鲁棒性又兼有较强的不可见性,具有切实的可行性。
基金supported by the Natural Science Foundation of Shanghai(18ZR1400300)
文摘To progressively provide the competitive rate-distortion performance for aerial imagery,a quantized block compressive sensing(QBCS) framework is presented,which incorporates two measurement-side control parameters:measurement subrate(S) and quantization depth(D).By learning how different parameter combinations may affect the quality-bitrate characteristics of aerial images,two parameter allocation models are derived between a bitrate budget and its appropriate parameters.Based on the corresponding allocation models,a model-guided image coding method is proposed to pre-determine the appropriate(S,D) combination for acquiring an aerial image via QBCS.The data-driven experimental results show that the proposed method can achieve near-optimal quality-bitrate performance under the QBCS framework.
