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.     

去雾算法在视频图像处理中的研究与应用

在有雾的天气条件下,针对空气能见度下降导致捕获的图像颜色退化、对比度降低的问题,需要对视频图像进行去雾处理.提出一种去雾算法应用于视频图像的处理过程中,该算法用最大像素值估计天空亮度,利用高斯低通滤波器分离出背景空气光,从而进行图像亮度恢复以及图像的饱和度校正.实验证明,该算法处理图像速度快,能应用于彩色图像与灰度图像的处理,同时可以获得较清晰的图像.     

基于WLS的雾天交通图像恢复方法

在尘雾等恶劣天气条件下,由于大气粒子的散射作用,致使获取的道路图像严重退化,给交通运输带来很大的困难.为了提高道路环境的可视性,文中提出了一种基于WLS的雾天交通图像恢复算法.该算法从大气散射模型出发,首先进行大气光照的估计与白平衡处理,然后结合道路环境的约束,构建WLS框架对大气耗散函数进行估计,从而恢复场景反照率.通过实验分析可知,文中算法能够有效去除图像中雾霾,消除了Halo效应的影响,较好地凸显图像远景的细节信息,实现了交通图像的视见度的提高.     

Study of single image dehazing algorithm based on propagated filtering and minimum color channels

To solve the challenging problem that the edge regions of image have some remained hazes and blackspots,a novel image dehazing algorithm was proposed based on the minimal color channel and propagated filtering.Firstly,an initial atmospheric transmission map was obtained by double-area filtering,then a minimal channel color was introduced as a reference image,and the propagated filtering was combined to optimize the initial atmospheric transmission map.Optimized transmission have the similar edge characteristics as the referenced image,so the deviation of transmission estimation can be effectively avoided for the edge pixels in the depth mutated regions,and the redundant texture information were removed in the initial transmission map.Finally,L-BFGS was used to restore atmospheric light,and the haze-free image can be recovered based on the atmospheric scattering model.Experimental results show that proposed algorithm has the more accurate transmission estimation for the depth mutated edge regions of image,so the recovered image effectively preserves the edges and details in the depth mutated regions,and has better spatial smoothness in the uniform depth regions.The recovered haze-free image with proposed method has a better sharpness and richer color degree.     

A High Quality Depth Map Upsampling Method Robust to Misalignment of Depth and Color Boundaries

In recent years, fusion camera systems that consist of color cameras and Time-of-Flight (TOF) depth sensors have been popularly used due to its depth sensing capability at real-time frame rates. However, captured depth maps are limited in low resolution compared to the corresponding color images due to physical limitation of the TOF depth sensor. Most approaches to enhancing the resolution of captured depth maps depend on the implicit assumption that when neighboring pixels in the color image have similar values, they are also similar in depth. Although many algorithms have been proposed, they still yield erroneous results, especially when region boundaries in the depth map and the color image are not aligned. We therefore propose a novel kernel regression framework to generate the high quality depth map. Our proposed filter is based on the vector pointing similar pixels that represents the unit vector toward similar neighbors in the local region. The vectors are used to detect misaligned regions between color edges and depth edges. Unlike conventional kernel regression methods, our method properly handles misaligned regions by introducing the numerical analysis of the local structure into the kernel regression framework. Experimental comparisons with other data fusion techniques prove the superiority of the proposed algorithm.     

一种快速的单幅图像去雾算法

为了解决基于暗原色先验的单幅图像去雾算法运行效率低,以及去雾后在图像的明亮灰白区域存在图像色彩失真的问题,提出一种快速有效的单幅图像去雾算法.该算法基于HSI色彩空间进行粗略的透射率以及大气光的估计;然后,用导向滤波对粗略的透射率进行平滑,再利用阈值法对灰白明亮区域的透射率进行修正,得到最终的透射率;最后,进行色彩调整得到复原图像.实验结果表明,本文算法具有很高的运行效率,能有效提高复原图像的清晰度和对比度.     

A lossless compression scheme for Bayer color filter array images.

In most digital cameras, Bayer color filter array (CFA) images are captured and demosaicing is generally carried out before compression. Recently, it was found that compression-first schemes outperform the conventional demosaicing-first schemes in terms of output image quality. An efficient prediction-based lossless compression scheme for Bayer CFA images is proposed in this paper. It exploits a context matching technique to rank the neighboring pixels when predicting a pixel, an adaptive color difference estimation scheme to remove the color spectral redundancy when handling red and blue samples, and an adaptive codeword generation technique to adjust the divisor of Rice code for encoding the prediction residues. Simulation results show that the proposed compression scheme can achieve a better compression performance than conventional lossless CFA image coding schemes.     

基于暗通道的单幅图像除雾

针对暗通道先验算法存在的对全局大气光值估计不准确的问题,提出了一种单幅图像除雾的优化技术,对全局大气光值的选取方式进行了改进。首先用完美反射法对含雾图像进行颜色校正,接下来利用四叉树算法粗略估计大气光值,对其进行数值校正;再利用引导滤波对透射率进行优化,进而得到平滑的透射率。通过大气散射模型复原,得到较为清晰的除雾图像,最后用引导滤波丰富图像细节。实验结果表明,该算法复原图像清晰度高,具有较好的视觉效果。     

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.     

Implementation of automatic white-balance based on adaptive-luminance

A novel automatic white-balance algorithm based on adaptive-luminance is proposed in this paper. This algorithm rede- fines the gray pixels region, which can filter the gray pixels accurately. Furthermore, with the relations between gray pixels’ luminance with standard light source and their chroma Cb, Cr shifts with other color temperatures, the algorithm estab- lishes the equations between the captured pixels and the original ones, which can estimate the gains of RGB channels exactly. To evaluate the prop...     

A new sampling algorithm for high-quality image matting

Image matting is the extraction of the foreground from an image through the use of provided information. It has been an important technique in the image and video editing field. Current image matting methods estimate the foreground and background, based on information provided regarding the nearby pixels. Color sampling has been an effective means for matting directly, and quite a few methods have achieved high quality matting results based on color sampling. However, there are some drawbacks; for example it is easy to overlook important candidate pixels for matting, and even if the candidate pixels are effectively selected, similar foregrounds and backgrounds will reduce the accuracy of the matting.In this paper’s work, a Weighted-Least Squares (WLS) filter was utilized to sharpen the boundaries between the foregrounds and backgrounds, which facilitated the matting process; and an innovative sampling criterion based on random searching for the matting was then presented. This innovative method could effectively prevent valid samples being overlooked, and could manage the relationships of the nearby and distant pixels. In this process, a new cost function was utilized to evaluate the candidate samples. Experiments utilizing an image database demonstrated that this method significantly improved the matting results.     

基于多光源模型的夜晚雾天图像去雾算法

依据夜间多光源导致强光处能见度低的现象,在传统大气散射模型中定义发光因子项,构建了一个专门针对夜晚雾天图像的去雾模型,在此基础上提出了一种夜晚雾天图像去雾算法.该算法将原输入图像分解为新雾天图像层和发光图像层,然后对此分解得到的新雾天图像层进行色偏纠正和引导滤波操作以得到最终的去雾结果.与暗原色原理方法、快速中值滤波方法、图像颜色迁移方法、夜晚成像模型方法等已有方法的对比实验证实了本文算法的有效性.该算法可应用于汽车防碰撞系统、道路监控系统,以及其他识别系统.     

基于雾线先验的时空关联约束视频去雾算法

现有视频去雾算法由于缺少对视频结构关联约束和帧间一致性分析,容易导致连续帧去雾结果在颜色和亮度上存在突变,同时去雾后的前景目标边缘区域也容易出现退化现象。针对上述问题,该文提出一种基于雾线先验的时空关联约束视频去雾算法,通过引入每帧图像在空间邻域中具有的结构关联性和时间邻域中具有的连续一致性,提高视频去雾算法的求解准确性和鲁棒性。算法首先使用暗通道先验估计每帧图像的大气光向量,并结合雾线先验求取初始透射率图。然后引入加权最小二乘边缘保持平滑滤波器对初始透射率图进行空间平滑,消除奇异点和噪声对估计结果的影响。进一步利用相机参数刻画连续帧间透射率图的时序变化规律,对独立求取的每帧透射率图进行时序关联修正。最后根据雾图模型获得最终的视频去雾结果。定性和定量的对比实验结果表明,该算法下视频去雾结果的帧间过渡更加自然,同时对每一帧图像的色彩还原更加准确,图像边缘的细节信息显示也更加丰富。     

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。     

尺度自适应暗通道先验去雾方法

在雾、霾等天气条件下,大气粒子的散射作用使环境的能见度偏低,视觉系统采集到的图像严重降质。基于暗通道先验的图像复原方法因其去雾效果自然、约束条件少,且易于实现等优点而受到广泛关注。但是,该方法的去雾效果受尺度（暗通道的求解半径）影响很大,对于不同场景的图像,不存在一个普遍适用的最优尺度。针对该问题,文中提出一种尺度自适应方法,根据图像的颜色和边缘特征自适应地调节暗通道的尺度范围,得到像素级的暗通道求解尺度,兼顾大尺度求解色彩失真小和小尺度求解光晕失真小等优点。此外,针对暗通道去雾方法会使天空光估计点落到前景区域的问题,提出了一种改进的天空光估计方法,可使估计点鲁棒地落到与其物理意义相符的背景区域。对多种雾化场景图像的处理结果表明:文中方法适应性强、去雾效果自然,且对比度提升显著。     

INPAINTING ALGORITHM FOR KINECT DEPTH MAP BASED ON FOREGROUND SEGMENTATION简

The depth information of the scene camera, and depth extraction is a key technology indicates the distance between the object and the in 3D video system. The emergence of Kinect makes the high resolution depth map capturing possible. However, the depth map captured by Kinect can not be directly used due to the existing holes and noises, which needs to be repaired. We propose a texture combined inpainting algorithm in this paper. Firstly, the foreground is segmented combined with the color characteristics of the texture image to repair the foreground of the depth map. Secondly, region growing is used to determine the match region of the hole in the depth map, and to accurately position the match region according to the texture information. Then the match region is weighted to fill the hole. Finally, a Gaussian filter is used to remove the noise in the depth map. Experimental results show that the proposed method can effectively repair the holes existing in the original depth map and get an accurate and smooth depth map, which can be used to render a virtual image with good quality.     

基于天空约束暗通道先验的图像去雾

针对现有暗通道图像去雾算法存在的天空色彩失真,景物边缘光晕效应等问题,本文提出了基于暗通道理论的改进去雾算法.由于暗原色先验理论不适用于天空区域,本文将引导滤波用于天空区域的细化分割,准确估计包含天空区域图像的大气光照强度,解决了天空色彩失真问题;其次,利用中值滤波得到详细边缘信息,进而得到更为清晰的透射率,有效抑制了景物边缘光晕问题;最后针对去雾后图像偏暗的问题,在HSV空间对亮度分量V通道进行增强处理.实验结果表明,针对带雾图像,本文算法能够有效地去雾,改善天空区域色彩失真以及景物边缘光晕问题.     

基于OTSU分割和融合的非均匀光照水下图像增强

针对深海和夜间水域补充照明造成的水下图像光照不均,水中悬浮颗粒造成的图像 噪声、低对比度、偏色等问题,提出一种新的非均匀光照水下图像增强方法。首先,用高 斯滤波去除水下图像的噪声;其次,用最大类间方差法(大律法,maximum inter class variance, OTSU)分割出图像的明暗区域 掩膜,将亮度图分割成明暗区域,并对暗区域进行同态滤波处理,校正光照不均造成的阴 影;接着,用加权平均法融合明暗区域得到新的亮度图,重新合成彩色图像;最后,对水 下图像用对比度受限自适应直方图均衡化(contrast limited adaptive histogram equalization, CLAHE)和灰度世界进行增强对比度和颜色失 真校正,得到增强的水下图像。实验结果表明,本文提出的算法能够有效的改善光照不均 问题,并去除水下图像噪声、增强图像对比度,有利于后续目标检测、追踪等任务的进 行。     

Moving object detection and tracking from video captured by moving camera

This paper presents an effective method for the detection and tracking of multiple moving objects from a video sequence captured by a moving camera without additional sensors. Moving object detection is relatively difficult for video captured by a moving camera, since camera motion and object motion are mixed. In the proposed method, the feature points in the frames are found and then classified as belonging to foreground or background features. Next, moving object regions are obtained using an integration scheme based on foreground feature points and foreground regions, which are obtained using an image difference scheme. Then, a compensation scheme based on the motion history of the continuous motion contours obtained from three consecutive frames is applied to increase the regions of moving objects. Moving objects are detected using a refinement scheme and a minimum bounding box. Finally, moving object tracking is achieved using a Kalman filter based on the center of gravity of a moving object region in the minimum bounding box. Experimental results show that the proposed method has good performance.     

Joint-adaptive bilateral depth map upsampling

In this paper, we present a new depth upsampler in which an upsampled depth map is computed at each pixel as the average of neighboring pixels, weighted by color and depth intensity filters. The proposed method features two parameters, an adaptive smoothing parameter and a control parameter. The adaptive smoothing parameter is determined based on the ratio between a depth map and its corresponding color image. The adaptive smoothing parameter is used to control the dynamic range of the color-range filter. The control parameter assigns a larger weighting factor to pixels in the object to which a missing pixel belongs. In a comparison with five existing upsamplers, the proposed method outperforms all five in terms of both objective and subjective quality.