利用水下平行光管测量水下相机成像分辨率

水下光学成像是重要的水下探测方式。现有水下相机成像检测方法受到水体本身以及测量方法的影响,难以准确进行成像分辨率检测。提出了基于水下平行光管的水下相机成像分辨率检测技术,通过在水中产生平行光束,直接对水下相机成像分辨率进行检测。通过仿真得出：水下平行光管在水中可见光和空气中单波长的调制传递函数(Modulation Transfer Function, MTF)基本一致。利用这一结论,提出了水下平行光管空气中装调检测的方法。针对实验室所研制的一款水下相机开展实验测试,其在水中可见光与空气中635 m光源照明条件下的分辨率相同。实验结果表明,所提出的基于水下平行光管的水下相机成像分辨率检测方法可有效消除水体对分辨率测量的影响,实现水下相机成像分辨率的准确测量。     

水下光电成像技术与装备研究进展(上)——水下激光距离选通技术

激光距离选通成像技术是当前国内外不断发展且应用有效的水下光电成像技术之一,国外20世纪90年代后陆续公开了一些水下光电成像探测系统和实验结果.结合海洋资源勘探、水下救援和侦察等应用领域的需求,全面介绍并对比分析了当前国内外典型水下激光距离选通成像装置和性能水平,讨论了其技术特点,对水下激光距离选通成像技术的发展和装置应...     

水下激光成像分辨率影响因素的理论分析

讨论了用调制传递函数（ＭＴＦ）表征水下激光成像质量特性及其影响因素的量值关系，给出了计算该系统ＭＴＦ的理论近似公式，得出了ＭＴＦ与激光束光斑的大小，目标的距离，接收视场角及光功率的数量关系，结果表明准直光斑的减少和采用窄视场的扫描技术能有效地提高成像的分辨率。     

激光水下目标单脉冲成像探测技术研究

从理论上对单脉冲成像系统的成像性能,包括成像能力、成像分辨率和成像帧速作了简要分析和计算.对单脉冲成像的实验系统和部分关键技术,如像增强、距离选通、降噪等作了具体介绍和技术分析,关键技术效果通过所示实验结果来具体说明.其试验及结果在国内属于首次公布. 实验研究清楚表明:激光水下目标单脉冲成像在理论和技术上是可行的,与其它成像手段相比具有明显优势,如何针对大目标、复杂目标、远距离目标以及较混沌水质实现单脉冲成像和目标识别是该课题技术研究的新内容.(OC29)     

激光水下成像技术及其进展

介绍了近年发展起来的三种主要的激光水下成像方法，即常规水下激光成像、高分辨率水下激光三维成像和偏振激光成像，分析了它们各自的工作原理、特点以及各自的发展状况。     

低级自适应光学能否用于水下成像

讨论了自适应光学技术用于水下成像的可能性。简要介绍了与穿过湍流成像有关的主要问题及这项技术的发展历史。指出了低价自适应光学技术的发展趋势，并给出用于矫正水中湍流畸变系统的使用结果。本文的目的是将目前用来矫正大气湍流的方法加以论证，以回答人们的期望实现的将同一技术用于水下成像和水下通信的有关问题。     

水下偏振清晰成像方法综述

水下成像环境复杂多变,在水下视觉的研究中会遇到许多典型的问题:在复杂的光学环境中,水下成像质量急剧下降,传统成像方法中常用的诸如颜色、亮度等特征衰减严重,难以有效地提高水下成像的质量。偏振成像可以对水下散射进行有效抑制,在水下成像环境中,分析目标信息光、后向散射光和前向散射光相应的偏振特性,针对性地解决不同分量对图像的影响进而实现图像质量的提高。基于水下成像物理模型、偏振成像原理详细阐述了水下偏振成像原理,着重论述了几种经典的水下偏振成像方法,总结了当前基于偏振特性的水下成像技术,并对其实际效果进行评价分析,依据现有的水下偏振成像技术的优缺点和实际成像效果对水下偏振成像技术的未来发展进行总结展望。     

基于距离选通的水下偏振光成像系统的研究

光在水下传输受到吸收和散射的影响,限制了水下成像的成像距离和成像清晰度。为能抑制后向散射光的干扰,结合距离选通技术和偏振成像技术的各自的特点,提出了基于距离选通的偏振成像方式。基于距离选通的偏振成像一方面利用同步控制装置,时间上分离目标反射光,另一方面通过水体散射光和物体散射光解偏振度的差异来实现成像。本文分析了基于距离选通的偏振成像原理,具体给出了实验装置和实验过程。通过实验分析,得出基于距离选通的偏振成像技术运用于水下成像的可行性,且能大大提高图像的对比度,使其成像质量优于距离选通方式。     

水下超视距三角形距离能量相关三维成像(特邀)

水下超视距三角形距离能量相关三维成像是一种新型的快速非扫描三维成像技术,可填补水下摄像机（空间分辨率高但作用距离近、无三维信息）与水下声呐（作用距离远但空间分辨率低）间的技术空白。介绍了水下距离能量相关三维成像国内外研究进展,并重点介绍中国科学院半导体研究所在水下成像方面开展的三角形距离能量相关三维成像的研究工作,提出了一种融合多脉冲延时积分的三角形距离能量相关三维成像,梳理了多脉冲延时积分景深调节技术下的典型时域工作参数,研制的水下激光选通三维成像系统绿瞳、凤眼和龙睛可实现探测距离大于4.8 AL的超视距百万像素三维成像,已用于渔网等微小目标探测、海洋生物原位探测、水下光学详查等应用中。     

基于自适应背景光提取的水下偏振去散射成像方法（特邀）

水下成像技术在水下目标探测和识别领域具有重大研究价值,而传统水下偏振成像技术往往无法同时满足成像质量与成像实时性。针对传统水下偏振去散射成像带来的数据量大、处理时间长等问题,提出了一种水下目标的快速、实时去散射清晰化偏振成像方法。该方法利用了背景光偏振度与目标光偏振度的差异性,用于自适应的快速获取背景散射光区域,同时延续了目标整体偏振度一致这一假设,利用加权直方图均匀化方法通过调整图像灰度级的动态范围,对不同频次的灰度级进行加权处理,得到更符合人眼视觉效果的图像。实验结果表明,快速水下偏振去散射技术在提升成像质量的同时有效的增快了处理时的运算速率,在水下工作中具有良好的应用前景。     

国外水下激光成象技术现状

国外水下激光成象技术现状冯包根（华中光电技术研究所武汉４３００７３）水下激光成象系统在军事上和海洋开发中都具有重要的实用价值。本文评述水下激光成象关键技术和水下蓝绿激光技术现状，介绍两种典型的国外水下激光成象系统并指出水下激光成象技术中今后重点研究课...     

基于LED辅助照明的水下图像增强算法研究

水下成像环境十分复杂,在成像过程中受到多种因素的影响,如光照的非均匀性,水对光的后向散射,成像器件的光学噪声,导致图像对比度低,细节模糊.针对水下图像的特点,提出运用同态滤波和双正交小波阈值滤波法相结合的图像增强算法改善水下图像光照的非均匀性、滤除噪声、提高水下图像的对比度,并通过峰值信噪比、均方差对该算法进行了性能评价.     

水声信道传输特性研究

在海洋开发中,基于水声的应用系统得到越来越广泛的应用。本文对水声信号的传播速度、传播损失、海洋噪声、信号带宽、多径传播复、多普勒效应等方面的性能做了详细分析,明确了水声信号的传输特性,对于水声应用系统研发有参考作用。     

Underwater image processing and analysis: A review

With increasing attentions being drawn to the underwater observation and utilization of marine resources in recent years, underwater image processing and analysis have become an active research hotspot. Different from the general images, marine environment is usually faced with some complicated situations such as underwater turbulence and diffusion, severe absorption and scattering of water body, various noises, low contrast, uniform illumination, monotonous color, complex underwater-background. In response to these typical challenges, a large body of works in underwater image processing has been exploited in recent years. This survey introduces a review of existing relatively mature and representative underwater image processing models, which are classified into seven categories including enhancement, fog removal, noise reduction, segmentation, salient object detection, color constancy and restoration. We then objectively evaluate the current situations and future development trend of underwater image processing, and provide some insights into the prospective research directions to promote the development of underwater vision and beyond.     

水声测距技术研究

水声信号相比其它信号在海水中能传得更远和更深,提出了水声测距的技术体制和几种水声信号测距处理方法,分析了海洋环境对水声测距的影响因素,对水声伪随机扩频测距的时间分辨率进行了试验测试,给出了用于同步测距的芯片级原子钟守时性能,对于提升水下高精度导航系统性能有一定参考价值。     

Underwater image processing method for fish localization and detection in submarine environment

Object detection is an important process in image processing, it aims to detect instances of semantic objects of a certain class in digital images and videos. Object detection has applications in many areas of computer vision such as underwater fish detection. In this paper we present a method for preprocessing and fish localization in underwater images. We are based on a Poisson–Gauss theory, because it can accurately describe the noise present in a large variety of imaging systems. In the preprocessing step we denoise and restore the raw images. These images are split into regions utilizing the mean shift algorithm. For each region, statistical estimation is done independently in order to combine regions into objects. The method is tested under different underwater conditions. Experimental results show that the proposed approach outperforms state of the art methods.     

Underwater acoustic sensor networks: research challenges

Underwater sensor nodes will find applications in oceanographic data collection, pollution monitoring, offshore exploration, disaster prevention, assisted navigation and tactical surveillance applications. Moreover, unmanned or autonomous underwater vehicles (UUVs, AUVs), equipped with sensors, will enable the exploration of natural undersea resources and gathering of scientific data in collaborative monitoring missions. Underwater acoustic networking is the enabling technology for these applications. Underwater networks consist of a variable number of sensors and vehicles that are deployed to perform collaborative monitoring tasks over a given area.In this paper, several fundamental key aspects of underwater acoustic communications are investigated. Different architectures for two-dimensional and three-dimensional underwater sensor networks are discussed, and the characteristics of the underwater channel are detailed. The main challenges for the development of efficient networking solutions posed by the underwater environment are detailed and a cross-layer approach to the integration of all communication functionalities is suggested. Furthermore, open research issues are discussed and possible solution approaches are outlined.     

Diving deeper into underwater image enhancement: A survey

The powerful representation capacity of deep learning has made it inevitable for the underwater image enhancement community to employ its potential. The exploration of deep underwater image enhancement networks is increasing over time; hence, a comprehensive survey is the need of the hour. In this paper, our main aim is two-fold, (1): to provide a comprehensive and in-depth survey of the deep learning-based underwater image enhancement, which covers various perspectives ranging from algorithms to open issues, and (2): to conduct a qualitative and quantitative comparison of the deep algorithms on diverse datasets to serve as a benchmark, which has been barely explored before.We first introduce the underwater image formation models, which are the base of training data synthesis and design of deep networks, and also helpful for understanding the process of underwater image degradation. Then, we review deep underwater image enhancement algorithms, and a glimpse of some of the aspects of the current networks is presented, including architecture, parameters, training data, loss function, and training configurations. We also summarize the evaluation metrics and underwater image datasets. Following that, a systematically experimental comparison is carried out to analyze the robustness and effectiveness of deep algorithms. Meanwhile, we point out the shortcomings of current benchmark datasets and evaluation metrics. Finally, we discuss several unsolved open issues and suggest possible research directions. We hope that all efforts done in this paper might serve as a comprehensive reference for future research and call for the development of deep learning-based underwater image enhancement.     

Low bit-rate compression of underwater image based on human visual system

Image plays an irreplaceable role compared with the text and sound in the underwater data collection and transmission researches. However, it suffers from the limited bandwidth of the underwater acoustic communication which cannot afford the large image data. Compressing the image data before transmission is an inevitable process in the underwater image communication. As usual, the natural image compression methods are directly applied to the underwater scene. As we all know, underwater image has different degradation from the natural one due to the optical transmission property. Low illumination in underwater will cause more seriously blurring and color fading than that in the air. It is a great challenge to decrease the bit-rate of the underwater image while preserving the compressed image quality as much as possible. In this paper, the Human Visual System (HVS) is taken into account during the compressing and the evaluating stages for the underwater image communication. We present a new methodology for underwater image compression. Firstly, by taking the human visual system into account, the chrominance perception operator is proposed in this paper to neglect the imperceptible chrominance shift which is widely exited in the underwater imaging to improve the image compression rate. Secondly, depth of field(DOF) of underwater image is usually shallow and most of the usable image has targets in it. An ROI extraction algorithm based on Boolean map detection is then used for the underwater image compression so as to reduce the bitrate of the compressed image. Furthermore, the underwater image is grainy and low contrast, that means the degradation happens in some regions of the image would not be perceived. Just notice difference(JND) sensing algorithm based on the spatial and frequency domain masking feature of HVS is also considered in the image processing. By combining the three aspects above, hybrid wavelet and asymmetric coding are used together to promote the underwater image compression, so that the image can have better quality and less redundancy. Experiments show that the proposed method can make full use of the inherent characteristics of underwater images, and maximize the visual redundancy of underwater images without reducing the visual perception quality of reconstructed images.     

水声网络通信性能分析

该文根据水声信道传播特性及水声网络结构特征,建立了基于水声网络的通信性能分析模型,并仿真分析了对等网络和汇聚网络两种水声网络结构的通信性能与节点间距、网络规模、工作繁忙度以及发射功率等因素的联系,结果表明通过适当的网络设置和协议设计能够优化网络的通信性能,并且存在使通信带宽效率最优的水声网络节点间距。该文的研究成果对水声网络应用具有指导意义。