基于红通道先验的水下障碍物双目定位方法

基于双目视觉的自主水下航行器在水下巡航过程中，由于水体对光线的衰减效应和悬浮颗粒对光线的散射作用，双目摄像机获取的图像存在对比度低、颜色失真等问题，导致水下障碍物定位的精度较低。针对以上问题，文中采用红通道先验复原算法提高水下成像质量，根据双目相机标定参数获取障碍物的双目视差图，并提出了一种基于深度视差图融合的水下障碍物定位方法。该方法通过融合深度视差图与水下复原轮廓图，对融合图像进行凸多边形检测，获取障碍物的轮廓，基于轮廓信息进行障碍物的有效深度信息提取，实现障碍物的空间定位。水下双目定位实验结果表明，文中所提方法可以使双目立体匹配的效果更理想，能够有效提高水下障碍物定位的精度。     

基于LCTF的水下光谱成像系统研制

水下光谱成像技术在水下目标物识别、海洋生态监测等领域有着重要作用。基于实际工程使用环境设计了基于液晶可调谐滤光片(LCTF)的水下光谱成像系统。该系统通过采用LCTF作为滤光结构以获得水下目标物的光谱信息。水下光谱成像系统在宽光谱LED光源的照明下,进行水池实验获得了目标物在波长400~700 nm之间的31个通道光谱图像。对水下具有相似颜色的不同物体的光谱信息进行了讨论和分析,结果表明:该系统有助于水下目标物识别和分类。在海试中对珊瑚进行了原位观测,成功获取了珊瑚礁的水下光谱图像。该系统有望应用于海洋遥感、海洋生态环境监测等领域。     

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。     

改进暗通道方法的水下强散射介质目标探测

针对水下场景目标探测图像质量退化问题,提出了一种自适应计算水体衰减系数暗通道融合多尺度Retinex(Multi-scale Retinex,MSR)的复原算法,有效实现了水下目标的复原。通过搭建的水下成像测量装置,借助成像系统获取水下模拟环境的探测图像,对水下探测图像按照算法流程图逐步处理,得到了有效复原水下目标辐射信息的图像。为客观评价算法的效果,采用对比度、平均梯度与信息熵作为定量评价指标因子,对该算法与常规三种算法进行了定量对比研究,结果表明,该算法处理结果各项定量评价指标因子均优于选取的对比算法。研究结果为水下目标探测提供了基础理论探索方法,对水下目标探测实施开展具有一定的指导意义。     

基于光谱重建技术的壁画颜色复原与评价

为了在给定的照明和观察条件下，用相机响应信号重建物体表面光谱反射率，实现颜色的高精度复原，采用了多光谱成像技术采集物体表面的多光谱图像，使用主成分分析、 R 矩阵和正则化 R 矩阵方法进行了光谱反射率重建的理论研究，并对壁画色块颜色复原进行了实验验证，取得了壁画色块的重建光谱和颜色复原数据，同时对基于正则化 R 矩阵方法的壁画色块颜色复原结果进行了评价。结果表明，正则化 R 矩阵方法进行光谱重建的光谱精度和色度精度更高，与主成分分析和 R 矩阵方法相比，色差降低了0.0732，适应度系数提高了1.10%，均方根误差降低了0.0035，光谱匹配偏指数降低了0.0225。该方法能够满足高精度颜色再现的需要，适用于文物艺术品数字化存档、文物艺术品修复等领域。     

基于激光偏振技术的海洋牧场水下成像技术研究

良好的水下视觉环境是推动海洋经济发展的前提,海洋牧场的建设对水下成像技术提 出 新的要求,但是受水体环境吸收及散射影响,光信息丢失严重,用常规光学成像方式进行水 下 图像获取会有可视距离短,图像模糊不清,对比度低等缺点。为解决海洋牧场水下成像清晰 度 低的问题,提出一种通过532 nm激光光源与偏振技术相结合的方法并 集成了一款水下相机。 532 nm波段的绿色激光光源在水体环境中受到水体影响较小,传播效 果更佳,偏振成像技术在去 背景光散射中应用广泛,采用532 nm激光光源与偏振成像技术相结合 的方法进行水下图像的获 取,并在模拟海水环境的水池中进行了海参图像获取的实验,实验结果证明在某一偏振角度 下, 532 nm激光光源与偏振成像技术相结合的方式获取的海参图像相比于 自然光下获取的原图像对比 度、清晰度更高,含有的图像信息更加丰富,验证了此方法在水下成像的可行性。     

全Stokes偏振关联成像技术研究

近年来,随着关联成像技术的高速发展,已被广泛应用于诸多领域内,并引起了高度关注。偏振探测技术能够区分不同材质物体,可以增强系统的探测识别能力。文中结合偏振探测和关联成像技术的优点,利用Walsh-Hadamard散斑对场景进行照明,并对场景反射光进行分时偏振探测,实现了对场景的全Stokes偏振关联成像。搭建相应的实验系统对多材质物体进行了成像实验,利用不同偏振探测信号与照明散斑计算并获得了物体的Stokes参数图像,实现了对同一场景中的不同材质物体和相同材质不同结构物体的区分。通过演化压缩采样复原技术,在不同采样率下对物体图像进行了复原,结果表明:演化压缩采样复原技术能在较低的采样率下,复原出清晰的场景全偏振信息。     

光谱编码计算关联成像技术研究

现有的多光谱成像技术通常采用光学分光的方式，使用多个探测器对成像场景的光谱图像进行采集，导致现有成像系统复杂，数据量大、效率低。针对现有技术的不足，提出基于正交调制模式的光谱编码计算关联成像技术。通过正交光谱编码矩阵融合Hadamard基图案构造投影散斑对宽带光源进行调制，单像素探测器收集成像物体与调制光源相互作用后的反射信号；应用演化压缩技术复原成像物体的混叠光谱图像；利用编码矩阵的正交性质解码出欠采样的光谱分量图像，对分离出的图像应用组稀疏压缩感知算法重构全采样的光谱分量图像，最后融合出成像物体的多光谱图像。通过数值模拟与实验两方面验证了所提方法的高效性。所提的技术简化了多光谱关联成像系统，降低了数据量。光谱编码方法可以扩展到更多的光谱通道，也可以应用在偏振关联成像、信息加密等领域。     

布尔混沌调制激光雷达水下目标成像实验研究

在激光雷达的水下目标探测中,浑浊水体中悬浮颗粒对激光的吸收和散射作用,使得激光在水中的传输严重衰减。尤其是后向散射所造成的噪声会降低目标对比度甚至淹没信号回波。载波调制技术可以有效抑制后向散射,提高目标回波信号的信噪比。文章将布尔混沌信号作为载波调制信号,基于其内禀高频强度调制特性,构建了布尔混沌调制激光雷达水下成像系统,并在实验室水箱环境下,对衰减系数不同的浑浊水体中的目标物体进行了三维成像实验研究,对比了不同水质中的目标物体三维成像效果。     

超视距可见光探测系统设计

在进行超视距成像时,由于物体表面反射的光线受到空气中悬浮颗粒的散射而发生衰减,导致图像对比度下降,同时物体周围环境中的光线也会被空气中的悬浮颗粒散射,散射后的光线进入成像设备又会导致图像颜色发生漂移.另外在能见度一定的情况下,随着成像距离的增加,可见光的大气透过率会持续下降,从而降低到达探测器的能量,影响成像质量.文中...     

基于颜色失真去除与暗通道先验的水下图像复原

水下图像成像过程与雾天图像虽然类似,但因水对光的选择性吸收和光的散射作用,水下图像存在颜色衰减并呈现蓝(绿)色基调,传统的去雾方法用于水下图像复原时效果欠佳。针对这类方法出现的缺点,该文根据先去除颜色失真后去除背景散射的思路,提出一种新的水下图像复原方法。结合光在水中的衰减特性,提出适用于水下图像的颜色失真去除方法,并利用散射系数与波长的关系修正各通道透射率;另外,该文改进的背景光估计方法可有效避免人工光源、白色物体、噪声等影响。实验结果证明,该文方法在恢复场景物体原本颜色和去除背景散射方面效果良好。     

Underwater image enhancement by wavelength compensation and dehazing

Light scattering and color change are two major sources of distortion for underwater photography. Light scattering is caused by light incident on objects reflected and deflected multiple times by particles present in the water before reaching the camera. This in turn lowers the visibility and contrast of the image captured. Color change corresponds to the varying degrees of attenuation encountered by light traveling in the water with different wavelengths, rendering ambient underwater environments dominated by a bluish tone. No existing underwater processing techniques can handle light scattering and color change distortions suffered by underwater images, and the possible presence of artificial lighting simultaneously. This paper proposes a novel systematic approach to enhance underwater images by a dehazing algorithm, to compensate the attenuation discrepancy along the propagation path, and to take the influence of the possible presence of an artifical light source into consideration. Once the depth map, i.e., distances between the objects and the camera, is estimated, the foreground and background within a scene are segmented. The light intensities of foreground and background are compared to determine whether an artificial light source is employed during the image capturing process. After compensating the effect of artifical light, the haze phenomenon and discrepancy in wavelength attenuation along the underwater propagation path to camera are corrected. Next, the water depth in the image scene is estimated according to the residual energy ratios of different color channels existing in the background light. Based on the amount of attenuation corresponding to each light wavelength, color change compensation is conducted to restore color balance. The performance of the proposed algorithm for wavelength compensation and image dehazing (WCID) is evaluated both objectively and subjectively by utilizing ground-truth color patches and video downloaded from the Youtube website. Both results demonstrate that images with significantly enhanced visibility and superior color fidelity are obtained by the WCID proposed.     

基于水下成像模型的图像清晰化算法

受水下场景中有机物和悬浮颗粒的影响,水下图像存在对比度低、颜色失真和细节丢失等问题。同时,水下场景中通常有人工光源存在,造成图像光照不均。传统基于图像去雾的方法用于水下图像复原时效果欠佳,为充分考虑水对光的吸收和散射作用,近期提出了新的水下成像模型和图像复原方法。但是这些方法未考虑红通道影响,导致估计的散射比偏大;另外,也未考虑人工光源的影响,导致估计的背景光过大。针对这些问题,该文提出一套有效的水下图像清晰化方案。首先,通过设置阈值确定是否将红通道信息用于暗通道计算,并将反映人工光源影响的饱和度指标用于散射比估计,以减小人工光源的影响。由此,提出了基于红通道预判和饱和度指标的暗通道计算方法。然后,根据三通道衰减系数比估计每个通道的透射率,可弥补目前很多方法假设蓝绿通道透射率一致的缺陷。最后,利用Shades of Gray算法估计环境光,并结合新的水下成像模型得到复原图像。实验结果表明,该文算法可显著提升图像的对比度,得到颜色自然、细节清晰的复原图像。     

基于背景光修正成像模型的水下图像复原

光在水下传播时由于受到水体吸收和散射作用的影响,导致水下图像质量严重退化。为了有效去除色偏和模糊,改善水下图像质量,该文提出一种基于背景光修正成像模型的水下图像复原方法。该方法基于对雾天图像的观察,提出了水下图像背景光偏移假设,并基于此建立背景光修正成像模型;随后使用单目深度估计网络获得场景深度的估计,并结合背景光修正的水下成像模型,利用非线性最小二乘拟合获得水下偏移分量的估计值从而实现水下图像去水;最后优化去水后的含雾图像的透射率,并结合修正后的背景光实现图像复原。实验结果表明,该文方法在恢复水下图像颜色和去除散射光方面效果良好。     

Visibility improvement of underwater turbid image using hybrid restoration network with weighted filter

Due to the attenuation of light passes through water, the captured underwater images suffer from low-contrast, halo artifacts, etc. To address this issue, the hybrid network with a weighted filter is proposed to improve the visibility of the obscured (turbid) images. In the captured image, the brighter pixels (near-to-source) are called foreground regions and the darker pixels (far-from-source) are called background regions. In order to ensure the adaptability of the proposed algorithm, the considered datasets are collected on different atmospheric light such as pond, lake, and fisheries tank. The foreground area of an image can be enhanced using the thresholding and masking technique. The background hazy region can be recovered by a hybrid Dehazenet called Generative Adversarial Network and Convolutional Neural Network. With this, the transmission map with high accuracy and color deviation can be addressed. Then both the regions are blended and the Amended Unsharp Mask filter is used to toughen the distorted edges. Finally, the blended restored image is weighted with a contrast factor to obtain the visibility improved image. The subjective and objective evaluation is done on considering the standard non-reference metric called Underwater Image Quality Measure comprises measures of color, sharpness, and contrast for a variety of water types with different atmospheric light. It is observed that the proposed technique showed a metric improvement of 57% compared to other existing techniques in an average manner. Overall, it is inferred that the proposed technique produces better results in both subjective and objective evaluation, thus it outperforms other state-of-the-art techniques.

     

Enhancing underwater image via color correction and Bi-interval contrast enhancement

Underwater captured images often suffer from color cast and low visibility due to light is scattered and absorbed while it traveling in water. In this paper, we proposed a novel method of color correction and Bi-interval contrast enhancement to improve the quality of underwater images. Firstly, a simple and effective color correction method based on sub-interval linear transformation is employed to address color distortion. Then, a Gaussian low-pass filter is applied to the L channel to decompose the low- and high-frequency components. Finally, the low- and high-frequency components are enhanced by Bi-interval histogram based on optimal equalization threshold strategy and S-shaped function to enhancement image contrast and highlight image details. Inspired by the multi-scale fusion, we employed a simple linear fusion to integrate the enhanced high- and low-frequency components. Comparison with state-of-the-art methods show that the proposed method outputs high-quality underwater images with qualitative and quantitative evaluation well.     

Color correction and restoration based on multi-scale recursive network for underwater optical image

Underwater image processing has played an important role in various fields such as submarine terrain scanning, submarine communication cable laying, underwater vehicles, underwater search and rescue. However, there are many difficulties in the process of acquiring underwater images. Specifically, the water body will selectively absorb part of the light when light travels through the water, resulting in color degradation of underwater images. At the same time, due to the influence of floating substances in the water, the light has a certain degree of scattering, which will bring serious problems such as blurred details and low contrast to underwater images. Therefore, using image processing technology to restore the real appearance of underwater images has a high practical value. In order to solve the above problems, we combine the color correction method with the deblurring network to improve the quality of underwater images in this paper. Firstly, aiming at the problem of insufficient number and diversity of underwater image samples, a network combined with depth image reconstruction and underwater image generation is proposed to simulate underwater images based on the style transfer method. Secondly, for the problem of color distortion, we propose a dynamic threshold color correction method based on image global information combined with the loss law of light propagation in water. Finally, in order to solve the problem of image blurring caused by scattering and further improve the overall image clarity, the color-corrected image is reconstructed by a multi-scale recursive convolutional neural network. Experiment results show that we can obtain images closer to underwater style with shorter training time. Compared with several latest underwater image processing methods, the proposed method has obvious advantages in multiple underwater scenes. Simultaneously, we can restore the color information, remove blurring and boost detail for underwater images.     

基于蓝绿通道自适应色彩补偿的水下图像增强

光在水中传播时受到水的吸收和悬浮粒子散射作用,导致水下图像颜色失真、对比度低、可视性差。针对上述退化问题,该文提出一种基于蓝绿通道自适应色彩补偿水下图像增强方法。首先,该方法分析水下成像模型的特点,根据蓝、绿色通道均值在3通道均值和的占比,将水下场景深度划分3个等级,利用光衰减率特性自适应补偿色彩,实现多场景色彩校正。然后对色彩补偿后的图像划分暗调、中间暗调、中间亮调、亮调4个区域,利用暗区域映射函数将图像暗区域映射到亮区域,在提升对比度的同时抑制噪声的产生。最后采用双线性插值解决分块处理产生的区域块效应。真实水下数据集实验结果表明,与现有方法相比,该方法可以提升多种场景的水下图像质量。     

Coordinated underwater dark channel prior for artifact removal of challenging image enhancement

When dehazing underwater images, the patch-by-patch dark channel prior (DCP) method is frequently used. After the DCP-based processing, there are still some drawbacks, such as patch artifacts, and these artifacts will seriously affect the subjective quality of some challenging images. To remove the patch artifacts from the DCP-guided enhancement mechanism, this paper proposes a coordinated underwater dark channel prior (CUDCP) method. The proposed method considers the characteristics of the red-green-blue channels with different attenuation situations, and thus the attenuation ratios of the red-green-blue channels are adaptively coordinated in diverse images. The requirement for color restoration is then assessed by an evaluation criterion, and the color restoration is carried out by using the compensated gray world (CGW) theory, which further coordinates the intensity of various red-green-blue channels. Our method next applies a patch-division average filter in accordance with the sub-patch classification. On the typical dataset, the enhanced images of our CUDCP method have higher average underwater image quality measure (UIQM) scores (about 2.274 8) when compared with the original images and those of some state-of-the-art enhancement methods, while the computational cost of CUDCP (about 88.618 8 s) is slightly higher than that of the original DCP (about 87.493 8 s). The experimental results demonstrate that in comparison to state-of-the-art enhancement methods, the proposed method can significantly reduce patch artifacts in challenging image enhancement, while maintaining the objective quality of such underwater images, and also enhancing their subjective quality at a reasonable computational cost.     

Automatic Red-Channel underwater image restoration

Underwater images typically exhibit color distortion and low contrast as a result of the exponential decay that light suffers as it travels. Moreover, colors associated to different wavelengths have different attenuation rates, being the red wavelength the one that attenuates the fastest. To restore underwater images, we propose a Red Channel method, where colors associated to short wavelengths are recovered, as expected for underwater images, leading to a recovery of the lost contrast. The Red Channel method can be interpreted as a variant of the Dark Channel method used for images degraded by the atmosphere when exposed to haze. Experimental results show that our technique handles gracefully artificially illuminated areas, and achieves a natural color correction and superior or equivalent visibility improvement when compared to other state-of-the-art methods.