基于颜色衰减先验和白平衡的水下图像复原

由于水体中悬浮颗粒对光线的吸收及散射作用,以及不同波长的光在水下的衰减程度不同,水下图像通常存在细节模糊、对比度低、颜色失真等问题。为改善水下图像质量,提出一种基于颜色衰减先验和白平衡的水下图像复原方法。首先,根据图像在HSV颜色空间中的颜色衰减先验获得场景深度图;其次,利用水下光学衰减特性估计RGB三通道对应的背景光强度与水下透射率,以实现水下图像的清晰化;最后,采用改进的白平衡方法对清晰化后的水下图像进行颜色校正。实验结果表明,该方法可以显著提升水下图像的细节清晰度与颜色保真度,视觉效果更接近自然场景下的图像。     

基于修正模型与暗通道先验信息的水下图像复原

受到复杂成像环境影响,光学视觉系统获取到的水下图像普遍存在对比度低、模糊和颜色失真等问题．为此,本文提出一种基于修正散射模型的水下图像复原算法．首先,深入分析光在水下的吸收衰减特性,在简化大气散射模型的基础上,将水体背景光融入到模型的直接衰减项;其次,考虑到水下红光迅速衰减,采用红通道的逆通道对其进行补偿;然后,使用基于四叉树的分级搜索算法估计水体背景光值;最后,在修正的成像模型基础上,结合水下暗通道先验信息估计介质透射率进而复原水下图像．实验结果表明,本文算法水下复原后的图像色彩自然,能有效恢复出远景区域的细节信息,图像对比度、色度和饱和度的综合评价指标整体优于对比算法,适用于不同类型的水下退化图像．     

基于衰减补偿与直方图拉伸的水下图像增强算法

水下物理环境复杂多变,导致获取的水下图像颜色失真、对比度低且细节模糊,影响了水下场景探测的准确性。结合衰减补偿和直方图拉伸技术,提出水下图像增强算法ACHS。根据不同颜色通道的衰减特性,设计基于衰减补偿的颜色校正方法解决水下图像颜色失真问题。将需要颜色校正的水下图像从RGB颜色模型转换到LAB颜色模型,使用引导滤波将亮度通道L分解为基础层和细节层,同时提出基于K-means聚类的双直方图增强算法用于增强基础层的对比度,通过Gamma校正突显细节层的纹理结构。在此基础上,累加亮度通道L的基础层和细节层,并将其从LAB颜色模型转换到RGB颜色模型以获取最终的增强图像。实验结果表明,与GDCP、REBE、WaterNet等算法相比,经该算法增强的水下图像可视度较高,并且具有自然的颜色和清晰的细节。     

基于水体衰减系数反演的水下图像复原方法

光在水中传输时容易受到水体和水中微粒的吸收和散射作用,造成成像模糊和颜色失真,导致水下图像质量有待提高。提出一种基于水体衰减系数反演的图像复原新方法,不预设水体类型和波长值,而是通过图像反演求得最符合图像特征的衰减系数。对图像去除垂直衰减色差,使描述水下图像强度规律的先验信息能更准确地反映水平方向的光传输特性,提升背景光估计和场景深度估计的准确性。利用水下图像的雾化模糊特征、红通道分量衰减特征以及通道间衰减差异特征得到水下图像场景深度的融合估计结果,依据像素场景深度与水下成像模型,通过非线性拟合直接反演出水下图像的衰减系数,并基于灰度世界假设对衰减系数进行优化,以完成传输率求解,实现图像复原。最后,使用非锐化掩膜增强图像,凸显细节信息,提升视觉效果。实验结果表明,与SMBOT、IBLA、ULAP、UDCP方法相比,该方法在RUIE数据集上的复原图像信息熵平均值分别提升了4.9%、5.4%、9.2%、17.9%,能较好地提升图像整体视觉效果。     

Recovering intrinsic images from a single image

Interpreting real-world images requires the ability distinguish the different characteristics of the scene that lead to its final appearance. Two of the most important of these characteristics are the shading and reflectance of each point in the scene. We present an algorithm that uses multiple cues to recover shading and reflectance intrinsic images from a single image. Using both color information and a classifier trained to recognize gray-scale patterns, given the lighting direction, each image derivative is classified as being caused by shading or a change in the surface's reflectance. The classifiers gather local evidence about the surface's form and color, which is then propagated using the Generalized Belief Propagation algorithm. The propagation step disambiguates areas of the image where the correct classification is not clear from local evidence. We use real-world images to demonstrate results and show how each component of the system affects the results.     

Active Polarization Descattering

Vision in scattering media is important but challenging. Images suffer from poor visibility due to backscattering and attenuation. Most prior methods for scene recovery use active illumination scanners (structured and gated), which can be slow and cumbersome, while natural illumination is inapplicable to dark environments. The current paper addresses the need for a non-scanning recovery method, that uses active scene irradiance. We study the formation of images under widefield artificial illumination. Based on the formation model, the paper presents an approach for recovering the object signal. It also yields rough information about the 3D scene structure. The approach can work with compact, simple hardware, having active widefield, polychromatic polarized illumination. The camera is fitted with a polarization analyzer. Two frames of the scene are taken, with different states of the analyzer or polarizer. A recovery algorithm follows the acquisition. It allows both the backscatter and the object reflection to be partially polarized. It thus unifies and generalizes prior polarization-based methods, which had assumed exclusive polarization of either of these components. The approach is limited to an effective range, due to image noise and illumination falloff. Thus, the limits and noise sensitivity are analyzed. We demonstrate the approach in underwater field experiments.     

Mid-water current aided localization for autonomous underwater vehicles

Survey-class autonomous underwater vehicles (AUVs) typically rely on Doppler Velocity Logs (DVL) for precision localization near the seafloor. In cases where the seafloor depth is greater than the DVL bottom-lock range, localizing between the surface and the seafloor presents a localization problem since both GPS and DVL observations are unavailable in the mid-water column. This work proposes a solution to this problem that exploits the fact that current profile layers of the water column are near constant over short time scales (in the scale of minutes). Using observations of these currents obtained with the Acoustic Doppler Current Profiler mode of the DVL during descent, along with data from other sensors, the method discussed herein constrains position error. The method is validated using field data from the Sirius AUV coupled with view-based Simultaneous Localization and Mapping (SLAM) and on descents up to 3km deep with the Sentry AUV.     

Seafloor map generation for autonomous underwater vehicle navigation

Elevation map generation is an essential component of any autonomous underwater vehicle designed to navigate close to the seafloor because elevation maps are used for obstacle avoidance, path planning and self localization. We present an algorithm for the reconstruction of elevation maps of the seafloor from side-scan sonar backscatter images and sparse bathymetric points co-registered within the image. Given the trajectory for the underwater vehicle, the reconstruction is corrected for the attitude of the side-scan sonar during the image generation process. To perform reconstruction, an arbitrary but computable scattering model is assumed for the seafloor backscatter. The algorithm uses the sparse bathymetric data to generate an initial estimate for the elevation map which is then iteratively refined to fit the backscatter image by minimizing a global error functional. Concurrently, the parameters of the scattering model are determined on a coarse grid in the image by fitting the assumed scattering model to the backscatter data. The reconstruction is corrected for the movement of the sensor by initially doing local reconstructions in sensor coordinates and then transforming the local reconstructions to a global coordinate system using vehicle attitude and performing the reconstruction again. We demonstrate the effectiveness of our algorithm on synthetic and real data sets. Our algorithm is shown to decrease the average elevation error when compared to real bathymetry from 4.6 meters for the initial surface estimate to 1.6 meters for the final surface estimate from a survey taken of the Juan de Fuca Ridge.     

Color balancing of digital photos using simple image statistics

The great diffusion of digital cameras and the widespread use of the internet have produced a mass of digital images depicting a huge variety of subjects, generally acquired by unknown imaging systems under unknown lighting conditions. This makes color balancing, recovery of the color characteristics of the original scene, increasingly difficult. In this paper, we describe a method for detecting and removing a color cast (i.e. a superimposed color due to lighting conditions, or to the characteristics of the capturing device), from a digital photo without any a priori knowledge of its semantic content. First a cast detector, using simple image statistics, classifies the input images as presenting no cast, evident cast, ambiguous cast, a predominant color that must be preserved (such as in underwater images or single color close-ups) or as unclassifiable. A cast remover, a modified version of the white balance algorithm, is then applied in cases of evident or ambiguous cast. The method we propose has been tested with positive results on a data set of some 750 photos.     

Visual Enhancement of Underwater Images Using Transmission Estimation and Multi-Scale Fusion

The demand for the exploration of ocean resources is increasing exponentially. Underwater image data plays a significant role in many research areas. Despite this, the visual quality of underwater images is degraded because of two main factors namely, backscattering and attenuation. Therefore, visual enhancement has become an essential process to recover the required data from the images. Many algorithms had been proposed in a decade for improving the quality of images. This paper aims to propose a single image enhancement technique without the use of any external datasets. For that, the degraded images are subjected to two main processes namely, color correction and image fusion. Initially, veiling light and transmission light is estimated to find the color required for correction. Veiling light refers to unwanted light, whereas transmission light refers to the required light for color correction. These estimated outputs are applied in the scene recovery equation. The image obtained from color correction is subjected to a fusion process where the image is categorized into two versions and applied to white balance and contrast enhancement techniques. The resultants are divided into three weight maps namely, luminance, saliency, chromaticity and fused using the Laplacian pyramid. The results obtained are graphically compared with their input data using RGB Histogram plot. Finally, image quality is measured and tabulated using underwater image quality measures.     

Multisensor integration for underwater scene classification

We describe a new approach for the classification of a seafloor that is imaged with high frequency sonar and optical sensors. Information from these sensors is combined to evaluate the material properties of the seafloor. Estimation of material properties is based on the phenomenological relationship between the acoustical image intensity, surface roughness, and intrinsic object properties in the underwater scene. The sonar image yields backscatter estimates, while the optical stereo imagery yields surface roughness parameters. These two pieces of information are combined by a composite roughness model of high-frequency bottom backscattering phenomenon. The model is based on the conservation of acoustic energy travelling across a fluid-fluid interface. The model provides estimates of material density ratio and sound velocity ratio for the seafloor. These parameters serve as physically meaningful features for classification of the seafloor. Experimental results using real data illustrate the usefulness of this approach for autonomous and/or remotely operated undersea activity.Supported by the National Science Foundation Research Initiation Award IRI-91109584.     

Simultaneous image color correction and enhancement using particle swarm optimization

Color images captured under various environments are often not ready to deliver the desired quality due to adverse effects caused by uncontrollable illumination settings. In particular, when the illuminate color is not known a priori, the colors of the objects may not be faithfully reproduced and thus impose difficulties in subsequent image processing operations. Color correction thus becomes a very important pre-processing procedure where the goal is to produce an image as if it is captured under uniform chromatic illumination. On the other hand, conventional color correction algorithms using linear gain adjustments focus only on color manipulations and may not convey the maximum information contained in the image. This challenge can be posed as a multi-objective optimization problem that simultaneously corrects the undesirable effect of illumination color cast while recovering the information conveyed from the scene. A variation of the particle swarm optimization algorithm is further developed in the multi-objective optimization perspective that results in a solution achieving a desirable color balance and an adequate delivery of information. Experiments are conducted using a collection of color images of natural objects that were captured under different lighting conditions. Results have shown that the proposed method is capable of delivering images with higher quality.     

偏振与ICA相结合的深度信息获取方法

针对水下图像中存在的图像退化现象,提出了一种将偏振与ICA技术相结合的深度信息获取方法。该方法先依据ICA模型对获取的偏振图像进行处理,构造已知偏振信息与水体后向散射光偏振度之间的线性关系,通过对获取的目标函数进行优化求解出水体后向散射光偏振度,最后采用软抠图法进行优化得到场景深度信息。实验结果表明,此方法在水下场景中获取的深度图像包含更多的目标信息,且运行效率高。     

Underwater image dehazing using joint trilateral filter

This paper describes a novel method to enhance underwater images by image dehazing. Scattering and color change are two major problems of distortion for underwater imaging. Scattering is caused by large suspended particles, such as turbid water which contains abundant particles. Color change or color distortion 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. Our key contributions are proposed a new underwater model to compensate the attenuation discrepancy along the propagation path, and proposed a fast joint trigonometric filtering dehazing algorithm. The enhanced images are characterized by reduced noised level, better exposedness of the dark regions, improved global contrast while the finest details and edges are enhanced significantly. In addition, our method is comparable to higher quality than the state-of-the-art methods by assuming in the latest image evaluation systems.     

Intrinsic Decompositions for Image Editing

Intrinsic images are a mid‐level representation of an image that decompose the image into reflectance and illumination layers. The reflectance layer captures the color/texture of surfaces in the scene, while the illumination layer captures shading effects caused by interactions between scene illumination and surface geometry. Intrinsic images have a long history in computer vision and recently in computer graphics, and have been shown to be a useful representation for tasks ranging from scene understanding and reconstruction to image editing. In this report, we review and evaluate past work on this problem. Specifically, we discuss each work in terms of the priors they impose on the intrinsic image problem. We introduce a new synthetic ground‐truth dataset that we use to evaluate the validity of these priors and the performance of the methods. Finally, we evaluate the performance of the different methods in the context of image‐editing applications.     

基于透射率修正的湍流模型与动态调整retinex的水下图像增强

目的 为提高水下获取的结构物表面缺陷图像的对比度和清晰度,便于缺陷区域的分割、提取和识别工作,提出了一种基于改进的湍流模型和引导滤波平滑的retinex的图像增强方法。方法 将光照不均的水下图像转换到Lab空间,对亮度空间进行自适应直方图均衡的匀光处理,根据暗通道先验理论估算匀光图像的透射率,结合大气湍流通用模型模拟退化图像,通过调整透射率系数获得退化图像。采用维纳滤波过滤图像噪声,将滤波后的图像作为导向图,利用导向滤波细化获得边缘保持的图像。根据3σ准则对3通道多尺度retinex （multi-scale retinex,MSR）的反射分量进行色彩矫正,获取最终增强后的水下结构物表面缺陷图像。结果 选取多组在不同湍流环境下采集的图像为研究对象,采用本文提出的方法进行实验,并与经典的暗通道算法、直方图均衡算法以及单尺度retinex算法对比,使用信噪比、信息熵、标准差和平均梯度等指标进行评估。实验结果表明,本文方法的信息熵、标准差相较直方图均衡算法和单尺度retinex分别提高了11.7%和25.6%,分割准确率上升了3.1%。从主观效果上看,本文算法图像细节更为丰富,视觉效果自然。结论 本文算法改善了退化模型的自适应问题,在信息熵、标准差、平均梯度等综合指标上均有优异表现,与暗通道先验方法相比,信噪比、平均梯度大幅提升,同时实现了缺陷的边缘保持效果,为下阶段的图像处理提供了良好的信息源。     

Water correction for improved benthic vegetation signal using satellite-borne hyperspectral data

Benthic mapping employs field surveys, hydroacoustic measurements, aerial photography, and satellite imagery. Effective benthic mapping involves removing overlying water effects from atmospherically corrected remotely sensed data to enhance signals from the seafloor. Our previous water correction algorithms depend on controlled laboratory measurements of substrates in clear water, which had challenges for replication. A more simplified water correction algorithm is presented, which uses bathymetry and only a few pixels from the image. Spectral profiles were extracted from four pixels in a Hyperspectral Imager for Coastal Oceans (HICO) image that was acquired in February 2014 over Indian River Lagoon, Florida. The four locations were chosen based on the assumption there were two types of homogeneous substrates at two depths. Our new algorithm calculates water column reflectance and water absorption at the instance of image data acquisition directly from the four pixel values. Water correction demonstrates improved benthic feature depiction including the near-infrared signals for benthic vegetation. A simple ratio was applied to the corrected image and demonstrates restored submerged vegetation signals.     

不同颜色模型下自适应直方图拉伸的水下图像增强

目的 水下图像是海洋信息的重要载体,然而与自然环境下的图像相比,其成像原理更复杂、对比度低、可视性差。为保证不同类型水下图像的增强效果,本文提出在两种颜色模型下自适应直方图拉伸的水下图像增强方法。方法 首先,进行基于Gray-World理论对蓝、绿色通道进行颜色均衡化预处理。然后,根据红绿蓝（R-G-B）通道的分布特性和不同颜色光线在水下传播时的选择性衰减,提出基于参数动态优化的R-G-B颜色模型自适应直方图拉伸,并采用引导滤波器降噪。接下来,在CIE-Lab颜色模型,对‘L’亮度和‘a’‘b’色彩分量分别进行线性和曲线自适应直方图拉伸优化。最终,增强的水下图像呈现出高对比度、均衡的饱和度和亮度。结果 选取不同类型的水下图像作为数据集,将本文方法与融合颜色模型（ICM）、非监督颜色纠正模型（UCM）、基于暗通道先验性（DCP）的水下图像复原和基于水下暗通道先验（UDCP）的图像复原方法相比较,增强后的图像具有高对比度和饱和度。定性和定量分析实验结果说明本文提出的方法能够获得更好视觉效果,增强后的图像拥有更高信息熵和较低噪声。结论 在RGB颜色模型中,通过合理地考虑水下图像的分布特性和水下图像退化物理模型提出自适应直方图拉伸方法;在CIE-Lab颜色模型中,引入拉伸函数和指数型曲线函数重分布色彩和亮度两个分量,本方法计算复杂度低,适用于不同复杂环境下的水下图像增强。     

基于暗原色先验原理的水下图像增强

针对水下光衰减和散射导致的图像严重降质问题和用传统方法进行水下图像增强 产生色偏现象,提出一种新的水下图像增强方法。基于暗原色先验原理进行水下图像增强,用 软抠图的方法对图像暗通道进行细化;在图像前0.1%最亮的像素点中,用中值滤波算法计算出 这些像素点的中值,再计算这些像素点和与之对应的中值的差值,差值最小的像素点作为背景 光的预估值,并用该像素点所在区域颜色饱和度方差来判断预估背景光的准确性;利用Retinex 算法和图像各颜色通道的衰减系数比对增强后的图像进行颜色校正。实验表明,该方法能有效 地去除水下图像中的雾色、校正图像色偏问题,进而提高图像对比度。     

Navigation of an AUV for investigation of underwater structures

There is a great demand for autonomous underwater vehicles (AUVs) to investigate artificial underwater structures such as piles and caissons in harbours, and risers and jackets of deep-sea oilfields. This paper proposes an autonomous investigation method of underwater structures using AUVs that is implemented by initially detecting the target objects, localizing them, then approaching them by taking video images while closely tracing their shape. A laser ranging system and a navigation method based on the relative position with respect to the target objects are introduced to realize this behaviour.