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。     

基于光衰减先验和背景光融合的水下图像复原

针对水下图像存在颜色失真和视觉模糊等问题,提出基于光衰减先验和背景光融合的水下图像复原算法.首先通过最大强度先验计算背景光一,基于图像四叉树的方法估计背景光二,根据水下图像光照的亮暗情况对两个局部背景光进行融合,确定全局背景光;其次根据光衰减先验估计场景的相对深度,进而计算三个通道的透射率;然后逆求解水下光学成像模型以...     

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。     

基于全变分和颜色平衡的水下图像复原

针对水下图像存在的雾化、模糊和颜色失真问题，提出一种基于全变分和颜色平衡的水下图像复原方法。以完整水下光学成像模型为基础，分别结合四叉树细分法与光在水中传播特性估计背景光，利用水下中值暗通道先验估计透射率，并采用共轭梯度和迭代最小二乘法估计模糊核。为提高计算效率，引入交替方向乘子法对变分能量方程进行逆求解得到去雾、去模糊的图像。在此基础上，在YCbCr空间采用颜色平衡算法对颜色通道进行补偿以校正色彩失真。与6种流行的水下图像增强和复原方法进行比较，实验结果表明，所提方法可以有效地去除雾化和模糊、校正色偏、恢复出清晰、色彩真实的水下图像。     

融合暗原色先验和稀疏表示的水下图像复原

由于水下图像成像过程中受光的散射、噪声干扰等因素影响,致使图像质量严重退化。为了去除模糊和抑制噪声,改善水下图像质量,该文提出一种融合暗原色先验和稀疏表示的水下图像复原新方法。该方法首先利用暗原色先验理论计算水下图像的暗原色,然后基于稀疏表示理论对暗原色进行去噪和优化,基于改进后的暗原色计算水体透射率和光照强度以计算最终复原结果,可以同时达到去模糊和去噪的良好效果。实验结果表明,提出的方法有效提高了图像的平均梯度和信息熵等图像像素,从而改善了图像的质量。     

结合改进红通道先验与幂律校正CLAHE的水下图像复原方法

针对水下降质图像复原过程中,存在背景光预估偏差及对比度失衡的问题,提出一种水下图像复原方法。首先根据超像素图像分割方法确定背景光区域及取值,然后采用红通道先验理论求取预估透射率,获得初步复原图像;最终通过归一化幂律校正的限制对比度自适应直方图均衡化（Contrast Limited Adaptive Histogram Equalization,CLAHE）算法增强复原图像的颜色。使用3种图像质量评价标准对实验结果进行客观分析,结果表明,该方法可以有效均衡对比度,提高可视化效果。     

基于场景深度估计和背景分割的水下图像复原

针对水下图像对比度低、颜色失真、可见度低等问题，提出了一种基于场景深度估计和背景区域分割的复原方法。首先，利用多方向斜梯度算子和各颜色通道的衰减差估计图像的场景深度。然后，利用场景深度估计过程中得到的梯度和色差信息将图像的背景与前景区域分离，并分别在背景和前景区域估计背景光和透射率。在得到背景光和透射率图后，基于水下成像模型对前景区域进行场景恢复，同时采用在HSV颜色空间直方图拉伸的方法对背景区域进行对比度增强。最后，通过设置过渡区域权重图对前景和背景进行融合得到最终的复原结果。实验结果表明，所提方法能更准确地估计背景光和透射率，在对比度增强、色彩修正及清晰度提升等方面具有良好的性能。与经典的方法对比，所提方法在UIQM、UCIQE、FDUM和FADE等4个客观质量评价指标上的提升均超过15%。     

偏振参数最优重构的水下降质图像清晰化方法

针对水体浑浊情况下,水中悬浮粒子对光的吸收和散射作用造成图像模糊、对比度低等问题,提出了一种偏振参数最优重构的水下降质图像清晰化方法。首先,通过局部最小值滤波估算水下背景光图像,引入 Stokes 矢量原理计算偏振度,通过归一化互信息进一步优化偏振度信息,获取成像区域最优的重构偏振参数;其次,采用形态学的方法重建图像自动估计水下无穷远处背景光值;最后,搭建了水下环境模拟平台,通过单通道偏振探测器实时获取水下偏振图像;为了验证算法的有效性,通过三种客观评价指标与其他复原方法进行比较,结果显示算法效果优于其他的水下图像复原方法。     

MATLAB在水下激光线扫描图像处理中的应用

MATLAB在数字图像处理中的应用越来越广泛。首先需要对水下激光线扫描系统成像获得的原始试验数据进行视频及图像处理才能完成对目标信息的重建。然后利用数学形态学方法进行图像处理,去除图像的背景光噪声。再用直方图均衡实现图像增强。实验结果表明应用MATLAB对图像进行形态学、直方图调整等方法能够有效消除图像的背景光噪声,提取出目标信息,提高信噪比,对水下激光成像效果的改善有显著效果。同时,相当于增大了水下激光成像的探测距离。     

基于暗通道先验和对比度增强的水下图像复原

在水下环境中,光的散射和衰减导致水下图像质量下降,为此提出一种基于暗通道先验和增强图像对比度的方法。根据水下图像成像特点,建立水下光学成像模型,采用改进的暗原色算法进行图像去模糊,由直方图均衡化和双边滤波器对去模糊之后的图像增强对比度。实验表明,该方法能够有效去除光的散射引起的模糊并提高图像对比度。     

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。     

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.     

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.     

一种用于水下图像的光谱重构方法:理论及应用

多光谱成像是一项非常有前景的图像高保真获取与再现技术,近年来在水下物体颜色还原的应用中也受到的极大的需求和关注。然而,不同于空气中的物体的成像过程,在水下成像过程中,当光通过水而进行传播,光被水体严重吸收和散射,导致图像变暗,在其光谱和颜色方面发生模糊和扭曲。文中讨论的是基于水下图像的水衰减系数的校准和其多光谱图像的光谱重构。首先在不同的距离处获取物体的图像,提出了基于不同距离的图像进行水体衰减系数的校准并恢复原始图像的技术;在此基础上,分析并导出满足系数校准和图像复原所需的在不同距离获取到的最少的原始图像个数。最后,通过比较复原的水下图像与空气中获取的彩色图像,实验结果证明:文中提出的技术能够对水下光谱图像的进行精确颜色复原,所有测试图像的平均相对残留误差仅为5.87％。     

Depth-aware total variation regularization for underwater image dehazing

Underwater images often show severe quality degradation due to the light absorption and scattering effects in water medium. This paper introduces a scene depth regularized underwater image dehazing method to obtain high-quality underwater images. Unlike previous underwater image dehazing methods that usually calculate a transmission map or a scene depth map using priors, we construct an exponential relationship between transmission map and normalized scene depth map. An initial scene depth is first estimated by the difference between color channels. Then it is refined by total variation regularization to keep structures while smoothing excessive details. An alternating direction algorithm is given to solve the optimization problem. Extensive experiments demonstrate that the proposed method can effectively improve the visual quality of degraded underwater images, and yields high-quality results comparative to the state-of-the-art underwater image enhancement methods quantitatively and qualitatively.     

Single underwater image haze removal with a learning-based approach to blurriness estimation

Underwater images are usually degraded due to light scattering and absorption. To recover the scene radiance of degraded underwater images, a new haze removal method is presented by incorporating a learning-based approach to blurriness estimation with the image formation model. Firstly, the image blurriness is estimated with a linear model trained on a set of selected grayscale images, the average Gaussian images and blurriness images. With the estimated image blurriness, three intermediate background lights (BLs) are computed to obtain the synthesized BL. Then the scene depth is calculated by using the estimated image blurriness and BL to construct a transmission map and restore the scene radiance. Compared with other haze removal methods, haze in degraded underwater images can be removed more accurately with our proposed method. Moreover, visual inspection, quantitative evaluation and application test demonstrate that our method is superior to the compared methods and beneficial to high-level vision tasks.     

结合线性景深和自适应雾浓度的去雾算法

针对图像去雾中由于景深和大气光估计不准确等问题,导致军事监测、目标检测、导航、无人驾驶等系统成像设备获取到的图像质量下降,提出一种结合线性景深估计和自适应雾浓度估计的去雾算法。首先,依照景深与亮度分量和饱和度的关系,利用双滤波优化二者高亮区域,结合线性转换建立线性模型估计景深。然后,提取纹理特征构造雾浓度模型求取自适应散射系数,通过所求景深与自适应散射系数得到透射率。最后,根据对雾图是否含有天空区域的判决,采用两种不同的大气光估计方法。实验结果通过与不同去雾算法定性和定量分析,所提出的方法在保留深度边缘、颜色质量及细节方面具有良好的有效性和鲁棒性,图像恢复质量也相对较佳。