基于压缩感知理论的水下成像技术和图像压缩编码技术研究
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摘要
压缩感知理论是近年来提出的一种新颖的数据采样指导理论。它改变了一直以来使用奈奎斯特定理为指导的采样模式，具有采样量少、节省采样资源等诸多优点，是近年来研究的热点。已在统计学、信息论、雷达成像和遥感等多个领域展开了应用研究。
作者在对水下光学进行调研时发现，水下光学成像技术在海洋学研究、水下机器人、水下科学考察、海洋资源考察、军事等领域有广泛的应用和需求。然而现有的水下光学成像技术在成像距离和成像质量方面难以满足实际需求。
通过对压缩感知理论的研究，本文在水下压缩感知成像及图像压缩编码两方面做了深入研究。
首次提出了水下压缩感知单像素相机技术的概念，构建了融合距离选通技术和激光主动照明技术的水下压缩感知单像素相机系统框架结构、SBHE采样矩阵和POCS重构算法。在系统软硬件设计的基础上，经过理论计算与现有水下成像技术在成像距离和成像质量上进行了比较。进行计算机模拟实验验证了该技术的可行性。通过理论分析和模拟实验说明了所提出的水下压缩感知单像素相机技术在提高成像距离和成像质量上的优越性。
首次提出一种基于光学稀疏的水下压缩感知成像新技术，建立了稀疏系统的两种工况下的数学模型，并提出两种相关的反演算法。通过对原始信号进行多次稀疏的方法，提高信号的稀疏度，达到减少压缩感知（CS）采样个数、提高采样效率的目的，按照这一思路构建了基于光学稀疏的水下压缩感知成像系统框架结构。根据照明光源的不同，分别建立了相干光和非相干光照明时的系统采样值与目标信号的数学模型。针对非相干光照明情况下，反演计算图像时的病态方程问题，提出一种1范数有约束最优化的算法。针对相干光照明情况下，系统反演时采样数据不包含相位信息无法进行逆傅立叶变换的问题，提出了一种替代值光波场复振幅反演的算法。并以非相干光照明情况为例，对系统进行了计算机模拟实验。实验结果表明本文方法能极大的提高采样效率和有效的提高成像质量。
首次提出了软距离选通技术。该技术针对硬距离选通技术成像距离固定、硬件和控制系统复杂、成本高等缺点，根据距离选通原理，利用水下压缩感知成像技术的特点，达到减少杂散光提高成像距离和质量的目的。同时，该技术具有成像灵活、系统简单、成本低廉和系统误差小等诸多优点。
提出一种新的基于混合采样和分块策略的图像压缩感知方法。该方法针对现有图像压缩感知方法采样效率较低的问题提出，利用低分辨率采样具有直接测量图像低频信息的特性，构造了基于互补的随机采样和低分辨率采样的混合采样矩阵，并理论证明了设计的混合采样矩阵的可行性，可以有效提高采样效率，而且结构简单、非常易于硬件实现；同时分块策略保证算法复杂度不随图像尺寸而改变，适合处理高分辨率图像。通过实验表明，该方法使用TV(total variation)重构算法时不仅极大地降低了计算复杂度，还可以有效提高重构质量，这对于压缩感知实时处理具有重要的意义。
Compressive Sensing (CS) is a new type of sampling theory. Compared with thetraditional Nyquist theorem, CS theory has many advantages, such as requiring fewersamples and less sampling resources. Recent results show that CS theory has greatpotential for many fields such as radar imaging, information processing and remotesensing. It is well known that underwater optical imaging has wide applications inoceanography, underwater scientific investigation, marine resource exploration andmilitary field, but the imaging distance and image quality of the existing underwateroptical imaging methods is hard to satisfy the actual demands. After deep research onthe superiority of CS theory on underwater imaging and image compression, someresearch results are taken in the dissertation as follows.
The concept of underwater single-pixel camera is first put forward. Using theadvantages of range gating in eliminating stray light when imaging and ofunderwater laser pulse illumination in increasing imaging beam energy, the framestructure of underwater single-pixel imaging system based on laser pulseillumination is designed. According to the designed system, some software platformssuch as Scrambled Block Hadamard Ensemble (SBHE) measurement matrix and Projections Onto Convex Sets (POCS) reconstruction algorithm are constructed.Comparisons between the proposed underwater imaging method and the existingunderwater imaging methods are also taken by the theoretical calculation on bothimaging distance and image quality. The feasibility of the proposed technology andthe necessity of the proposed frame structure are fully proved by computersimulation experiments. Meanwhile, the superiority of the proposed underwaterimaging technology is fully shown by the theoretical analysis and the simulationexperiments.
Owing to the complicated characteristics of underwater optical imagingenvironment, a novel active illumination compressive sensing-based underwaterimaging system using the optical sparsity method is first proposed to increase thesampling efficiency and to decrease the sampling time of the underwatercompressive sensing-based imaging system. By increasing the sparsity of the imagesignal via optical sparsity, it is easy to reach the aim of reducing compressive sensingmeasurements. The system framework of the proposed method is also constructed.According to different light sources, the mathematical models of the proposedsystems using coherent illumination and non-coherent illumination are made up inorder to determine the mathematical relations between the measurements and thetarget signal. In non-coherent illumination case, the constrainedl1-normoptimization algorithm is proposed to overcome the ill-posed equation problemoccurred in the image inverse calculation process. In coherent illumination case, aninverse algorithm with the complex amplitude of light wave field replacing by thespecific value is put forward to solve the problem that the Fourier inverse transformis not implemented because the sampled data in the inverse system do not containphase information. The results obtained from the computer simulation experimentswhen using the non-coherent illumination case as an example shows that theproposed method increases sampling efficiency and improves image quality greatly.
In order to overcome the disadvantages of the traditional rage gating, such asthe fixed imaging distance, the complicated hardware and control system and high cost, a new range gating is proposed to reach the goal of increasing imaging distanceand image quality using the principle of range gating and the superiority ofunderwater compressive sensing-based imaging system to reducing the stray light.Meanwhile, the proposed method has many advantages such as flexible imaging,simple system, low cost and less system errors.
A new compressive sensing method based on hybrid sampling and blockstrategy (BHCS) is proposed for images to improve the low sampling efficiencyproblem existing in image compressive sensing algorithms available. In the method,a hybrid measurement matrix combining random sampling (RS) and low-resolutionsampling (LRS) is designed to complementally measure the image information datawith high sensing efficiency using the fact that low-resolution sampling canefficiently measure the low-frequency information in image signals. Further, thehybrid measurement matrix with simple structure is proved theoretically to beincoherent with most fixed sparsifying bases. And block strategy ensures that thecomplexity of measurement and reconstruction processes does not change due to theimage size, so the method is simple and easy to implement, suitable for large-scaleapplications. Experimental results show that the proposed method using totalvariation (TV) reconstruction algorithm achieves much better results than manystate-of-the-art algorithms in terms of both PSNR and visual perception，whichmeans a lot to the application of CS theory on the field of real-time processing.
作者在对水下光学进行调研时发现，水下光学成像技术在海洋学研究、水下机器人、水下科学考察、海洋资源考察、军事等领域有广泛的应用和需求。然而现有的水下光学成像技术在成像距离和成像质量方面难以满足实际需求。
通过对压缩感知理论的研究，本文在水下压缩感知成像及图像压缩编码两方面做了深入研究。
首次提出了水下压缩感知单像素相机技术的概念，构建了融合距离选通技术和激光主动照明技术的水下压缩感知单像素相机系统框架结构、SBHE采样矩阵和POCS重构算法。在系统软硬件设计的基础上，经过理论计算与现有水下成像技术在成像距离和成像质量上进行了比较。进行计算机模拟实验验证了该技术的可行性。通过理论分析和模拟实验说明了所提出的水下压缩感知单像素相机技术在提高成像距离和成像质量上的优越性。
首次提出一种基于光学稀疏的水下压缩感知成像新技术，建立了稀疏系统的两种工况下的数学模型，并提出两种相关的反演算法。通过对原始信号进行多次稀疏的方法，提高信号的稀疏度，达到减少压缩感知（CS）采样个数、提高采样效率的目的，按照这一思路构建了基于光学稀疏的水下压缩感知成像系统框架结构。根据照明光源的不同，分别建立了相干光和非相干光照明时的系统采样值与目标信号的数学模型。针对非相干光照明情况下，反演计算图像时的病态方程问题，提出一种1范数有约束最优化的算法。针对相干光照明情况下，系统反演时采样数据不包含相位信息无法进行逆傅立叶变换的问题，提出了一种替代值光波场复振幅反演的算法。并以非相干光照明情况为例，对系统进行了计算机模拟实验。实验结果表明本文方法能极大的提高采样效率和有效的提高成像质量。
首次提出了软距离选通技术。该技术针对硬距离选通技术成像距离固定、硬件和控制系统复杂、成本高等缺点，根据距离选通原理，利用水下压缩感知成像技术的特点，达到减少杂散光提高成像距离和质量的目的。同时，该技术具有成像灵活、系统简单、成本低廉和系统误差小等诸多优点。
提出一种新的基于混合采样和分块策略的图像压缩感知方法。该方法针对现有图像压缩感知方法采样效率较低的问题提出，利用低分辨率采样具有直接测量图像低频信息的特性，构造了基于互补的随机采样和低分辨率采样的混合采样矩阵，并理论证明了设计的混合采样矩阵的可行性，可以有效提高采样效率，而且结构简单、非常易于硬件实现；同时分块策略保证算法复杂度不随图像尺寸而改变，适合处理高分辨率图像。通过实验表明，该方法使用TV(total variation)重构算法时不仅极大地降低了计算复杂度，还可以有效提高重构质量，这对于压缩感知实时处理具有重要的意义。
Compressive Sensing (CS) is a new type of sampling theory. Compared with thetraditional Nyquist theorem, CS theory has many advantages, such as requiring fewersamples and less sampling resources. Recent results show that CS theory has greatpotential for many fields such as radar imaging, information processing and remotesensing. It is well known that underwater optical imaging has wide applications inoceanography, underwater scientific investigation, marine resource exploration andmilitary field, but the imaging distance and image quality of the existing underwateroptical imaging methods is hard to satisfy the actual demands. After deep research onthe superiority of CS theory on underwater imaging and image compression, someresearch results are taken in the dissertation as follows.
The concept of underwater single-pixel camera is first put forward. Using theadvantages of range gating in eliminating stray light when imaging and ofunderwater laser pulse illumination in increasing imaging beam energy, the framestructure of underwater single-pixel imaging system based on laser pulseillumination is designed. According to the designed system, some software platformssuch as Scrambled Block Hadamard Ensemble (SBHE) measurement matrix and Projections Onto Convex Sets (POCS) reconstruction algorithm are constructed.Comparisons between the proposed underwater imaging method and the existingunderwater imaging methods are also taken by the theoretical calculation on bothimaging distance and image quality. The feasibility of the proposed technology andthe necessity of the proposed frame structure are fully proved by computersimulation experiments. Meanwhile, the superiority of the proposed underwaterimaging technology is fully shown by the theoretical analysis and the simulationexperiments.
Owing to the complicated characteristics of underwater optical imagingenvironment, a novel active illumination compressive sensing-based underwaterimaging system using the optical sparsity method is first proposed to increase thesampling efficiency and to decrease the sampling time of the underwatercompressive sensing-based imaging system. By increasing the sparsity of the imagesignal via optical sparsity, it is easy to reach the aim of reducing compressive sensingmeasurements. The system framework of the proposed method is also constructed.According to different light sources, the mathematical models of the proposedsystems using coherent illumination and non-coherent illumination are made up inorder to determine the mathematical relations between the measurements and thetarget signal. In non-coherent illumination case, the constrainedl1-normoptimization algorithm is proposed to overcome the ill-posed equation problemoccurred in the image inverse calculation process. In coherent illumination case, aninverse algorithm with the complex amplitude of light wave field replacing by thespecific value is put forward to solve the problem that the Fourier inverse transformis not implemented because the sampled data in the inverse system do not containphase information. The results obtained from the computer simulation experimentswhen using the non-coherent illumination case as an example shows that theproposed method increases sampling efficiency and improves image quality greatly.
In order to overcome the disadvantages of the traditional rage gating, such asthe fixed imaging distance, the complicated hardware and control system and high cost, a new range gating is proposed to reach the goal of increasing imaging distanceand image quality using the principle of range gating and the superiority ofunderwater compressive sensing-based imaging system to reducing the stray light.Meanwhile, the proposed method has many advantages such as flexible imaging,simple system, low cost and less system errors.
A new compressive sensing method based on hybrid sampling and blockstrategy (BHCS) is proposed for images to improve the low sampling efficiencyproblem existing in image compressive sensing algorithms available. In the method,a hybrid measurement matrix combining random sampling (RS) and low-resolutionsampling (LRS) is designed to complementally measure the image information datawith high sensing efficiency using the fact that low-resolution sampling canefficiently measure the low-frequency information in image signals. Further, thehybrid measurement matrix with simple structure is proved theoretically to beincoherent with most fixed sparsifying bases. And block strategy ensures that thecomplexity of measurement and reconstruction processes does not change due to theimage size, so the method is simple and easy to implement, suitable for large-scaleapplications. Experimental results show that the proposed method using totalvariation (TV) reconstruction algorithm achieves much better results than manystate-of-the-art algorithms in terms of both PSNR and visual perception，whichmeans a lot to the application of CS theory on the field of real-time processing.
引文
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