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。     

大气散射模型下衰减系数对图像去雾的影响

本文针对当前在大气散射模型下的去雾算法存在图像质量较差问题,从模型原理上分析衰减系数的变化对于去雾后图像质量的影响.通过大气光成像的物理建模和数学分析,逐步推导出衰减系数,分析了其物理意义.结合颜色衰减先验去雾算法,并改变衰减系数,得到不同的效果图,根据图像的评价参数,对比分析衰减系数会带来的影响.实验显示,衰减系数的变化能在一定程度上影响图像的色彩、对比度,进而影响去雾的实际效果,可以通过适当的改变衰减系数优化图像去雾的效果.     

基于雾线和颜色衰减先验的图像去雾方法

针对现有图像去雾方法易产生的颜色过饱和、细节丢失、伪影等问题，提出了一种基于雾线和颜色衰减先验的去雾方法。首先，利用雾线先验和霍夫投票估计大气光。然后，根据颜色衰减先验建立关于场景深度的非线性模型，获取准确的透射率。最后，通过对大气散射模型进行反向求解去除图像中的雾霾干扰，获得细节丰富的去雾图像。在RESIDE公共数据集上进行了实验，并与多种现有方法进行了比较。实验结果表明，所提方法可有效去除图像中的雾霾干扰，获得清晰自然的去雾图像，且其时间和空间效率均优于其他方法。     

基于颜色空间的单幅图像去雾算法

为改善雾天图像对比度差、能见度低的特点,本文结合雾天成像模型和暗原色先验规律,在颜色空间的基础上提出了一种去雾新算法。首先,在RGB颜色空间,根据暗原色先验规律估计出空气光,然后将图像从RGB颜色空间转换到HSI和HSV颜色空间,再对HSV空间下的明度分量运用大气散射模型进行去雾处理,最后再对HSI空间下的饱和度分量进行校正,最终得到去雾之后的图像。通过该算法能得到清晰化的图像,并且该算法较之传统的单幅图像去雾方法,速度更快、效果更自然。     

区域分割优化的暗通道先验去雾算法

较为经典的去雾算法是暗通道先验去雾算法,但该算法会产生天空区域光晕失真现象。为此,提出区域分割优化的改进暗通道先验去雾算法。首先提出K均值聚类分割算法,得到区域类别标签图;以大气光值选取区域为掩膜,求出类别标签图中天空区域的类别标签值,依据二值分类法将类别标签图转换为包含天空区域和前景区域的二值图;然后对透射率图进行区域优化处理,克服暗通道先验去雾算法天空区域低估透射率的不足;最后将有雾图像代入有雾图像退化模型,还原出颜色逼真的无雾图像。利用不同去雾算法进行去雾对比实验,结果表明,该算法消除了天空区域的光晕失真现象,去雾图像细节获得明显增强,去雾效果优于其他去雾算法,实验验证了该算法的正确性和可行性。     

基于饱和度运算单幅图像去雾算法

在雾、霾等恶劣的天气条件下,大气介质中悬浮粒子的散射和吸收作用会严重退化户外拍摄图像,造成图像识别率降低.从单色大气散射模型和暗原色先验规律,提出面向视觉感知的HSI颜色模型的饱和度的新算法,从而实现图像去雾,对于去雾图像最小值像素点采用极大值和极小值进行估计,并对透射率进行修正.该算法能够有效地提高清晰度,能很好地运用于单幅图像去雾.     

改进的雾线先验透射率优化图像去雾算法

针对传统去雾算法处理图像后存在颜色不均衡、能见度较低等问题,提出一种基于雾线先验的双边滤波优化透射率算法。首先,将像素在RGB空间中聚类成雾线并引入自适应模块对大气光值进行预估。将大气散射模型和上下文正则化原理相结合,对图像透射率进行初步优化,同时基于最小通道对透射率进行更正,使得传输率图更加平滑,防止相邻景深区域透射率差距过大;再经过双边滤波对透射率进行二次优化,使其变得更加精准。然后,将大气光值和透射率输入到大气散射模型进行去雾处理得到无雾图像。最后,将去雾后的图像进行色彩增强,以提升图像的色彩真实性和亮度。实验结果表明,所提方法在主观上提升了人眼的视觉效果,在客观评价指标结构相似性(SSIM)、峰值信噪比(PSNR)、角点检测数、通用质量指数(UQI)、自然图像质量评估(NIQE)和处理时长上均有着显著的优越性。     

一种改进的暗通道先验去雾算法研究

为了解决经典暗通道先验算法颜色偏暗、过饱和,天空区域出现光晕、噪声等问题,提出了一种改进的暗通道先验去雾算法.首先,通过对大气光的物理意义进行分析,将图像天空区域的亮通道值作为估值;然后针对传统去雾方法先验条件改进透射率估计,根据Hue-Saturation-Intensity颜色模型的图像增强结果计算粗透射率;之后使...     

图像去雾中深度学习数据增强方法

图像去雾是图像处理领域中非常重要的问题。深度学习可以有效提高图像清晰度,但训练过程中由于缺少相对应的真实雾匹配数据对,多采用合成雾作为数据集。现有合成雾多依赖于深度信息、大气散射系数等参数,针对由此作为数据集训练容易造成颜色失真和去雾不彻底的问题,提出基于循环生成对抗网络(CycleGAN)合成雾方法。通过该网络进行不匹配数据对训练学习有雾图像的特征,然后赋予清晰图片真实雾特征并与其自身构成匹配数据对,最后再用此类数据集进行去雾训练。结果表明,这些数据集可以有效解决颜色失真和去雾不彻底等问题。     

基于暗通道先验的图像去雾算法改进

为了实现基于物理模型的图像复原去雾算法,文中提出了一种改进的基于暗通道先验的图像去雾算法。介绍了雾天图像退化模型和基于该雾天图像退化模型的几种去雾算法。详细介绍了何恺明提出的基于暗通道先验的去雾算法,该算法在估计光线传播图时使用的基于导向滤波的软抠图非常耗时,经过改进,直接使用景深估计光线传播图,算法运行时间大大减少。最后,使用MATLAB对改进的去雾算法进行仿真,并与原算法的运行时间进行比较。结果显示新方法对光线传播图的估计可靠,运行时间对比改进前大约下降60%,实时性大大提高。带有天空的有雾图像去雾后色斑和光晕大幅减少,取得了很好的效果。改进的去雾算法运行速度快、去雾效果好,新提出的光线传播图估计方法可靠,并且去雾过程中得到的光线传播图可以用于其他应用。     

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。     

基于颜色衰减先验和视觉显著性的水下图像增强算法

针对水下拍摄的图片存在颜色失真、细节和边缘模 糊等特点,提出了一种基于颜色衰减先验的水下图像增强算法。首先在计算暗通道函数时,用最小值滤波去噪。然后,对图片进行显著图处理,利用颜色先验法则完成深度估计。此滤波方法不仅能降噪,还可以防止颜色失真。最后,基于模型简化获得复原的图片,将其进行伽马变换进行校正,实现柔性去雾。实验结果表明,本文算法与几种典型的水下图像去雾算法相比,能够较好提高图像的清晰度和对比度,同时获得较好的图像颜色。     

Image restoration algorithm based on compensated transmission and adaptive haze concentration coefficient

Aiming at the drawbacks of traditional dark channel prior,which was prone to distortion and Halo effects in the bright areas,a haze image restoration algorithm based on compensated transmission and adaptive haze concentration coefficient was proposed.First of all,a Gaussian function was used to fit the attenuation relationship between the haze and haze-free image,and the compensation transmission was set to correct the initial transmission.Then the characteristics of haze was analyzed,the concept of brightness entropy was introduced and the bright channel operation was performed to acquire entropy value with pixel by pixel.Combined with the Gaussian pyramid to extract texture features,the haze distribution map was obtained.An adaptive transformation was established to seek the haze concentration coefficient and get the accurate transmission.Finally,the recovery results were restored by improved atmospheric light value and atmospheric scattering model.Experimental results show that the recovered image has better color and detail,the degree of dehazing is thorough,the brightness is appropriate,and the effect is clear and natural.     

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.     

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。     

基于FPGA的视频图像去雾系统的设计与实现

基于FPGA设计与实现视频图像实时去雾系统.该系统基于暗原色去雾模型,直接估算雾的浓度并恢复出高质量的去雾图像,具有并行运算能力强、接口逻辑丰富等特性,为构建实时、便携的视频图像实时去雾系统提供了一种有效、可行的解决方案.实验结果表明,通过合理的硬件架构设计,该系统完全可达到视频去雾的实时处理.     

A joint cumulative distribution function and gradient fusion based method for dehazing of long shot hazy images

Hazy or foggy weather conditions significantly degrade the visual quality of an image in an outdoor environment. It also changes the color and reduces the contrast of an image. This paper introduces a novel single image dehazing technique to restore a hazy image without considering the physical model of haze formation. In order to find haze-free image, the proposed method does not require the transmission map and its costly refinement process. Since haze effect is dependent on the depth, it severely degrades the visibility of the objects located at a far distance. The objects close to the camera are unaffected. In this paper, we propose a fusion-based haze removal method based on the joint cumulative distribution function (JCDF) that treats faraway haze and nearby haze separately. The output images after the JCDF module, fused in the gradient domain to produce a haze-free image. The proposed method not only significantly enhances visibility but also preserves texture details. The proposed method is experimented and evaluated on a large set of challenging hazy images (large scene depth, night time, dense fog, etc.). Both qualitative and quantitative measures show that the performance of the proposed method is better than the state-of-the-art dehazing techniques.     

A multi-exposure fusion framework for contrast enhancement of hazy images employing dynamic stochastic resonance

Current imaging devices coupled with advanced hardware and software are smart enough to enhance low light images taken in clear weather. But in hazy or foggy environments, the captured images are of degraded quality. To address this issue, image processing algorithms are employed to enhance the degraded images to make useful for extracting meaningful features. In this study, we propose a haze removal algorithm to improve the color and contrast of images captured in hazy environments. The first step involves generation of images with various exposures using the theory of dynamic stochastic resonance. The images are then fused in a multi-scale fusion framework crafting weight maps viz. haze density, chromaticity, and luminance gradient. The fusion process focuses on uniformly enhancing the dark and bright regions of the image. However, it may overemphasize haze affected regions. Therefore, in the second step, the atmospheric scattering equation is referred and its modified version is applied that accomplishes the haze removal task. Quantitative and qualitative analyses demonstrate the effectiveness of the proposed method.