AIDEDNet: anti-interference and detail enhancement dehazing network for real-world scenes

The haze phenomenon seriously interferes the image acquisition and reduces image quality. Due to many uncertain factors, dehazing is typically a challenge in image processing. The most existing deep learning-based dehazing approaches apply the atmospheric scattering model (ASM) or a similar physical model, which originally comes from traditional dehazing methods. However, the data set trained in deep learning does not match well this model for three reasons. Firstly, the atmospheric illumination in ASM is obtained from prior experience, which is not accurate for dehazing real-scene. Secondly, it is difficult to get the depth of outdoor scenes for ASM. Thirdly, the haze is a complex natural phenomenon, and it is difficult to find an accurate physical model and related parameters to describe this phenomenon. In this paper, we propose a black box method, in which the haze is considered an image quality problem without using any physical model such as ASM. Analytically, we propose a novel dehazing equation to combine two mechanisms: interference item and detail enhancement item. The interference item estimates the haze information for dehazing the image, and then the detail enhancement item can repair and enhance the details of the dehazed image. Based on the new equation, we design an anti-interference and detail enhancement dehazing network (AIDEDNet), which is dramatically different from existing dehazing networks in that our network is fed into the haze-free images for training. Specifically, we propose a new way to construct a haze patch on the flight of network training. The patch is randomly selected from the input images and the thickness of haze is also randomly set. Numerous experiment results show that AIDEDNet outperforms the state-of-the-art methods on both synthetic haze scenes and real-world haze scenes.     

去光源影响的加权导向滤波图像去雾算法

含光源影响的雾图像中光源极易引入雾天原本不存在的光晕,从而影响大气光值和透射率估算的准确率,针对此问题,提出一种简单、易实现的去光源影响的雾天图像去雾算法.首先,基于超像素分割在CIELab颜色空间进行光源区域确定;然后,引入基于距离度量的光衰减因子计算并去除光源的影响;最后,以超像素块为单位估计大气光值,并采用加权导...     

海天背景图像去雾方法研究

海天背景图像存在大面积天空区域,且在远距离平视时目标一定出现在海天线附近,现有的去雾算法对天空区域的改善都是弱化天空区域的处理,这样势必会造成海天线附近去雾效果的减弱,不利于后续的目标检测.针对该问题,提出一种基于大气散射模型的图像复原去雾算法.首先,利用海天背景图像的特点,采用边缘检测算子将图像划分为天空和非天空区域,并结合大气光的物理意义,取天空区域最大的值作为大气光的估计值;其次,针对有雾图像对比度很低而无雾图像对比度较高这一先验信息设计代价函数,并通过SLIC超像素分割进行分块,通过求解每个小块内该函数的最小值,估计出粗透射率,再用引导滤波对粗透射率进行细化从而消除块效应;最后,利用大气散射模型,代入前两步求得的参数便可以得到恢复的无雾图像.实验结果与分析结果表明,本文能对海天背景的图像取得较好的去雾效果.     

基于视觉物理模型的去雾算法

针对现有图像去雾算法不能有效增强复杂大气环境下退化图像的问题,结合单色 大气散射模型、大气传输函数(ATF)以及Retinex 提出了一种基于视觉物理模型(VPM)的图像去 雾算法。新模型可同时描述非均匀光照退化、雾霾退化以及噪声退化等复杂大气环境下的图像 退化。模型求解过程首先使用变分法消除环境光退化,然后引入马尔科夫随机场将场景反射率 求解问题转换为了最大后验概率问题,最后利用对比度抑制自适应直方图均衡来校正场景反射 率亮度,从而实现图像去雾。实验结果表明VPM 能够指复杂大气环境下退化图像的增强,使 其物理保真度和视觉愉悦性得到有效改善。     

利用偏振滤波的自动图像去雾

针对雾天退化图像提出一种自适应图像复原方法。 该方法基于定义的偏振图像暗通道, 自动提取图像中的天空区域, 由此获得大气光的强度和偏振度; 采用偏振滤波提取大气光强信息, 并基于最小归一化互信息原则对估计的大气光偏振度进行优化; 根据大气光强的变化规律, 对大气光强的分布进行修复; 将大气光强作为加性噪声予以扣除, 并补偿因大气衰减带来的影响, 最终复原得到场景的辐射强度信息。 实验结果表明, 该方法能够有效地改善雾天下图像的退化现象, 提高了图像的清晰度。     

Vision and the Atmosphere

Current vision systems are designed to perform in clear weather. Needless to say, in any outdoor application, there is no escape from bad weather. Ultimately, computer vision systems must include mechanisms that enable them to function (even if somewhat less reliably) in the presence of haze, fog, rain, hail and snow.We begin by studying the visual manifestations of different weather conditions. For this, we draw on what is already known about atmospheric optics, and identify effects caused by bad weather that can be turned to our advantage. Since the atmosphere modulates the information carried from a scene point to the observer, it can be viewed as a mechanism of visual information coding. We exploit two fundamental scattering models and develop methods for recovering pertinent scene properties, such as three-dimensional structure, from one or two images taken under poor weather conditions.Next, we model the chromatic effects of the atmospheric scattering and verify it for fog and haze. Based on this chromatic model we derive several geometric constraints on scene color changes caused by varying atmospheric conditions. Finally, using these constraints we develop algorithms for computing fog or haze color, depth segmentation, extracting three-dimensional structure, and recovering clear day scene colors, from two or more images taken under different but unknown weather conditions.