Weakly supervised single image dehazing

Single image dehazing is a critical image pre-processing step for many practical vision systems. Most existing dehazing methods solve this problem utilizing various of hand-crafted priors or by supervised training on the synthetic hazy image information (such as haze-free image, transmission map and atmospheric light). However, the assumptions on the hand-crafted priors are easily violated and collecting realistic transmission map and atmospheric light are unpractical. In this paper, we propose a novel weakly supervised network based on the multi-level multi-scale block. The proposed network reduces the constraint on the training data and automatically estimates the transmission map and the atmospheric light as well as the intermediate haze-free image without using any realistic transmission map and atmospheric light as supervision. Moreover, the estimated intermediate haze-free image helps to generate accurate transmission map and atmospheric light by embedding the physical-model, which presents reliable restoration of the final haze-free image. In particular, our network also can be trained on the real-world dataset to fine-tune the model and the fine-tuning operation improves the dehazing performance on the real-world dataset. Quantitative and qualitative experimental results demonstrate the proposed method performs on par with the supervised methods.     

Variational optimization based single image dehazing

In this paper, we present a new approach for single image dehazing based on the proposed variational optimization. A hazy image captures the information about haze in terms of the transmission map and object details present in it. We propose to estimate the initial transmission map by performing the structure-aware smoothing of the hazy image. Further, we formulated a variational optimization for the estimation of final transmission, which refines the initial transmission of a hazy image. Atmospheric light can be considered to be constant throughout the scene for practical purposes. The uniform atmospheric light is computed from the dark channel of a hazy image. The exhaustive experimentation shows that the performance of the proposed method is comparable or better.     

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.     

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.     

基于区间估计的单幅图像快速去雾

针对雾、霾等天气条件下捕获的图像存在严重降质现象,该文提出一种基于区间估计的单幅图像快速去雾方法。该方法从大气散射模型出发,基于暗通道先验理论,利用最小值滤波和灰度开运算,通过区间估计得到大气光值,同时得到介质传输率的初始估计值。通过对大气光照进行白平衡处理,从而得到简化大气散射模型。然后,利用简化大气散射模型和介质传输率的初始估计值,通过区间估计得到场景反照率的暗通道值,进一步得到介质传输率的粗略估计值。将介质传输率的初始估计值和粗略估计值进行像素级融合,通过联合双边滤波和值域调整得到介质传输率的最终估计值。最后,通过简化大气散射模型和色调调整得到去雾图像。实验结果表明,所提算法具有较快的运算速度,能有效提高去雾图像的清晰度和对比度,同时获得较好的色调保真度。     

基于半反图像的透射率优化降雾算法

传统的暗原色先验图像降雾算法在处理不满足暗原色先验假设的明亮区域时,估计的透射率不准确。从而导致降雾后的图像色彩出现较大偏差。针对这一不足,本文提出了一种基于半反图像的透射率优化降雾算法。该算法通过明亮区域检测来获取大气光,然后用自定义函数对图像中明亮区域透射率进行修正,最后利用引导滤波器优化初始透射率,恢复出清晰的降雾图像。实验结果表明,该算法可以有效地处理图像中不满足暗原色先验假设的明亮区域,提高了户外视觉系统的鲁棒性。     

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 modular architecture for high resolution image dehazing

Haze is an atmospheric phenomenon which diminishes visibility in outdoor images. Algorithms based on dark channel prior (DCP) and haze line prior are found to be effective for dehazing images. These two methods make use of the Laplacian matrix, which is computationally complex, memory intensive and slow, thus making it impossible to use them on high-resolution (large) images. Multiple strategies have been suggested in the literature to speed up dehazing process by avoiding the Laplacian matrix, but these methods compromise on the quality of dehazing. We propose an effective modular method which divides the input image into blocks and processes each block independently. This makes it possible to use our method for dehazing large images retaining Laplacian matting and thus ensuring the output image quality. This division results in the possibility of assuming local values of atmospheric light. We show that this approach results in better dehazing in the local regions. The effectiveness of the proposed modular architecture is tested also on a learning based method. The output of the modular method is compared with those of different state-of-the-art dehazing methods for multiple quality parameters. Toward this, we have created a dataset of hazy natural outdoor images of large size.     

基于显著性权重的多曝光融合的单幅雾天图像复原算法

传统暗原色理论的相关算法,在处理雾天图像时会产生颜色的畸变和亮度的损失,针对该情况,该文提出基于权重多曝光融合的单幅雾天图像复原算法.首先通过雾天图像的直方图分析,获取全局大气背景光值的区域.其次构造一种新的Kirsh算子的高阶差分滤波方法,优化透射率图像.最后设计一种基于显著性权重的多曝光图像融合方法,提高处理后图像...     

图像去雾过程中的噪声抑制方法

大气中微小颗粒（如雾、霾等）的散射作用会使户外场景拍摄的图像发生退化,造成图像质量下降。图像去雾可以提升图像对比度,增加场景能见度,校正颜色失真,改善视觉效果。但是图像去雾经常会出现明显的噪声放大现象,尤其是无穷远处的天空区域最为严重。针对这一问题,提出了一种去雾过程中的噪声抑制方法。以传输率图像为指导,采用滤波半径变化的双边滤波对雾天图像进行模糊。再计算新的传输率图像,代入雾天成像模型,得到去噪后复原图像。结合噪声评价方法,实验结果验证了该方法的噪声抑制效果。     

基于霾层学习的单幅图像去雾算法

针对传统去雾算法出现色彩失真、去雾不完全、出现光晕等现象,本文提出了一种基于霾层学习的卷积神经网络的单幅图像去雾算法.首先,依据大气散射物理模型进行理论推导,本文设计了一种能够直接学习和估计有雾图像和霾层图像之间的映射关系的网络模型.采用有雾图像作为输入,并输出有雾图像与无雾图像之间的残差图像,随后直接从有雾图像中去除此霾层图像,即可恢复出无雾图像.残差学习的引入,使得网络来直接估计初始霾层,利用相对大的学习率,减少计算量,加快收敛过程.再利用引导滤波进行细化,使得恢复出的无雾图像更接近真实场景.本文对不同雾浓度的有雾图片的去雾效果进行测试,并与当前主流深度学习去雾算法及其他经典算法进行对比.实验结果显示,本文设计的卷积神经网络模型在图像去雾的应用,不论在主观效果还是客观指标上,都有优势.     

基于雾气特征与不等关系约束的去雾方法

针对现有去雾算法未充分考虑图像雾气信息、复原图像细节模糊等问题,提出一种新颖的反映图像雾信息分布的雾气特征图,并采用不等关系约束方法提高图像质量。首先,提取退化图像的极值通道以实现雾气信息的粗略估计,并通过L-1正则化对其进行优化从而得到雾气特征图。其次,提出一种基于雾气特征的初级大气光幕函数,通过对颜色通道和大气光幕作深入分析,利用均值不等式获得约束后的退化场景大气光幕。最后,利用雾气特征图对局部大气光进行改进,并基于大气散射模型实现图像去雾。将所提算法在真实雾图和合成数据集雾图上与其他经典方法进行比较分析,可以发现,所提算法在单幅图像去雾中展现了较好的性能,且在夜间雾图复原中更具优势。     

单幅雾天图像复原

 本文提出了一种单幅雾天图像分割复原的方法,首先利用黑体原理估计粗糙的大气光传输图,然后使用拉普拉斯修补矩阵对分割之后的传输图进行修补,当场景目标和大气光很类似时,采用置信传播推断算法纠正传输图,经过修补和推断之后的传输图能够准确反映光线通过雾的传输过程,结合雾天图像光学模型,从雾的物理特性上去除雾对图像的影响.实验结果表明,本文提出的雾天图像分割复原算法能够有效恢复出清晰图像,并能获取相应雾天图像深度信息.     

Visibility improvement of underwater turbid image using hybrid restoration network with weighted filter

Due to the attenuation of light passes through water, the captured underwater images suffer from low-contrast, halo artifacts, etc. To address this issue, the hybrid network with a weighted filter is proposed to improve the visibility of the obscured (turbid) images. In the captured image, the brighter pixels (near-to-source) are called foreground regions and the darker pixels (far-from-source) are called background regions. In order to ensure the adaptability of the proposed algorithm, the considered datasets are collected on different atmospheric light such as pond, lake, and fisheries tank. The foreground area of an image can be enhanced using the thresholding and masking technique. The background hazy region can be recovered by a hybrid Dehazenet called Generative Adversarial Network and Convolutional Neural Network. With this, the transmission map with high accuracy and color deviation can be addressed. Then both the regions are blended and the Amended Unsharp Mask filter is used to toughen the distorted edges. Finally, the blended restored image is weighted with a contrast factor to obtain the visibility improved image. The subjective and objective evaluation is done on considering the standard non-reference metric called Underwater Image Quality Measure comprises measures of color, sharpness, and contrast for a variety of water types with different atmospheric light. It is observed that the proposed technique showed a metric improvement of 57% compared to other existing techniques in an average manner. Overall, it is inferred that the proposed technique produces better results in both subjective and objective evaluation, thus it outperforms other state-of-the-art techniques.

     

基于非局部全变分正则化优化的单幅雾天图像恢复新方法

该文针对无雾图像具有高灰度对比度且大气遮罩局部平滑的特性,提出一种基于非局部全变分正则化优化的单幅雾天图像恢复新方法。先构建一种基于非局部全变分正则化的有约束优化算法对大气遮罩进行估计,然后通过优化Bregman分离迭代法求解非局部Rudin-Osher-Fatemi模型获得准确的大气遮罩,进而从雾天场景图像恢复出场景图像。实验结果表明,所提新方法可以有效地对雾天降质图像进行复原,对多纹理复杂区域的恢复效果也较好。     

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。     

基于非局部先验的雾线优化图像去雾算法

针对非局部先验去雾算法中雾线端点像素位置精确度不足的问题,提出了雾线优化的非局部先验图像去雾算法。首先分析雾线理论,结合暗通道理论确定最大聚类雾线真实端点,以其为已知条件补偿小聚类雾线端点与大气光之间的距离,根据类内不同像素与雾线对应夹角预估单个像素雾线端点进而求得像素级优化后的透射率,最后根据图像局部灰度值差异融合暗通道先验(dark channel prior, DCP)和非局部先验透射率得最终透射率图。将本文算法与其余3种去雾算法在多幅户外雾图下通过主观及客观两方面分析比较,实验结果表明该算法能取得更好的去雾效果,尤其在天空区域图像复原效果较为突出。     

Single Image Dehazing Based on Combining Dark Channel Prior and Scene Radiance Constraint

Images captured in foggy or hazy weather conditions often suffer from poor visibility.The dark channel prior method has well solved the single image dehazing problem in nature,but it is invalid when the scene objects are inherently similar to the atmospheric light and no shadow is cast on them.We propose an efficient regularization method by adding a scene radiance constraint and combing the dark channel prior to remove hazes from a sin-gle input image.The experiments show that this improved algorithm can deal with various levels of foggy weather conditions,as well as greatly enhance the image's visibility and details.In addition,the recovered haze-free image has little or no halo artifacts.     

基于多先验约束和一致性正则的半监督图像去雾算法

针对合成雾霾图像训练的去雾模型在真实场景中去雾效果不佳、对高层视觉任务性能提升不明显等问题,该文提出一种基于多先验约束和一致性正则的半监督图像去雾算法。该方法采用编码器-解码器网络结构,同时在合成雾霾图像与真实雾霾图像上学习去雾映射,并利用多种统计先验去雾结果作为真实雾霾图像参考真值进行半监督学习,同时通过多张真实雾霾图像的随机混合进行一致性正则约束,以消除多种先验去雾结果差异以及噪声干扰,提高图像去雾结果的视觉质量。实验对比结果表明,所提算法可比现有方法获得更好的真实场景去雾结果,并且能够显著提升高层视觉任务性能。     

基于区间估计的快速单幅图像去雾方法

单幅图像去雾是一个病态反问题。为了解决这个问题,对于大气光强度和介质传输率的估计已进行了大量研究。本文从新的角度提出一种简单但有效的基于区间估计的单幅图像快速去雾方法。该方法从大气散射模型出发,首先,通过对大气光强度和介质传输率取值范围的讨论,减小区间估计的范围。其次,通过快速联合双边滤波和中值滤波对介质传输率进行边缘优化,并利用大气光强度与暗通道差值绝对值大小对介质传输率中明亮区域进行判断和修正。最后,通过色调调整对复原图像进行整体优化。实验结果表明,与几种典型的图像去雾方法比较,本文方法明显提高了算法运算速度,并获得了较好的复原效果。