基于双注意力机制的雾天图像清晰化算法研究

针对传统去雾算法容易依赖先验知识以及恢复出来的清晰图像会产生颜色失真等问题,本文提出一种基于双注意力机制的雾天图像清晰化算法。首先将雾图输入编码器,经过下采样后得到特征图像;特征提取模块将多个特征提取基本块联结在一起,每个基本块由局部残差学习和特征注意模块组成,提高图像质量以及图像特征信息的利用率,增加网络训练的稳定性;然后通过通道注意力与多尺度空间注意力并行的结构处理特征图像,使得网络更加关注细节特征,提取更多关键信息,同时提高网络效率;最后将融合后的特征图像输入解码器中,经过多级映射,得到与输入大小匹配的雾密度图。实验结果表明,不论是对合成雾天图像或者真实雾天图像,本文算法能够高效地进行去雾处理,得到更自然的清晰图像。     

Adaptive single image dehazing method based on support vector machine

A dehazing method often only shows good results when processing the image for a certain haze concentration. So an adaptive hazy image dehazing method based on SVM is proposed. The innovation points are as follows: Firstly, combining the characteristics of the degraded images of haze weather, the dark channel histogram and texture features of the input images are extracted to form the feature vectors. These are trained by supervised learning through SVM algorithm to realize automatic binary classification of images; Secondly, the defined dehazing methods are called to process the classified result as a hazy image and the same quality evaluation indexes are used to evaluate each image output by different dehazing methods. Then, it outputs the highest evaluation image after haze removal. Finally, the output image is classified again by SVM until the image reaches the clearest it can be. The experimental results show that the proposed algorithm exhibits good contrast, brightness and color saturation from the visual effect. Also the scene adaptability and robustness of the algorithm are improved.     

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。     

基于多尺度特征结合细节恢复的单幅图像去雾方法

为提高单幅图像去雾方法的准确性及其去雾结果的细节可见性,该文提出一种基于多尺度特征结合细节恢复的单幅图像去雾方法。首先,根据雾在图像中的分布特性及成像原理,设计多尺度特征提取模块及多尺度特征融合模块,从而有效提取有雾图像中与雾相关的多尺度特征并进行非线性加权融合。其次,构造基于所设计多尺度特征提取模块和多尺度特征融合模块的端到端去雾网络,并利用该网络获得初步去雾结果。再次,构造基于图像分块的细节恢复网络以提取细节信息。最后,将细节恢复网络提取出的细节信息与去雾网络得到的初步去雾结果融合得到最终清晰的去雾图像,实现对去雾后图像视觉效果的增强。实验结果表明,与已有代表性的图像去雾方法相比,所提方法能够对合成图像及真实图像中的雾进行有效去除,且去雾结果细节信息保留完整。     

基于Wiener滤波的快速视频去雾算法

雾霾环境会极大的降低视频中事物的能见度。针对于目前去雾算法存在运算复杂度大、处理时间长的缺点,提出一种基于Wiener滤波的快速去雾算法,首先获取暗原色模型,通过Wiener滤波自适应获取透射率分布图并进行初级及深度去雾,最后通过自适应对数变换进行亮度调整,得到去雾后图像。实验证明,相比现有的先进方法,本文的算法具有处理速度快和去雾效果好的优点。     

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FPGA的高清实时视频去雾系统的设计与实现

实现了基于Xilinx XC6SLX45 FPGA的高清实时视频去雾系统,详细介绍了系统的逻辑框架及主要的算法实现.首先,介绍了整个系统的硬件框架及FPGA内部的逻辑框架;其次,描述了改进的暗原色先验去雾算法,给出了在FPGA中的算法实现过程;最后,对系统进行测试.实验结果表明,该系统实现了预期的高清视频实时去雾,并具有自适应功能,去雾效果良好.     

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.     

基于快速引导滤波改进的暗原色先验去雾算法

针对目前单幅雾霾降质图像存在大面积天空域,导致暗原色失效复原图像失真以及去雾时间复杂度高的问题.提出一种基于暗原色先验和快速引导滤波的去雾方法,针对存在高亮天空域图像多尺度滤波采用自适应阈值分割得到天空域,在天空域求取精确大气光值,再将天空域和非天空域透射率有效归一化,最后采用暗通道图作为引导图快速引导滤波精细化透射率,最大限度保留边缘细节的同时有效降低时间复杂度.实验结果表明,该方法可以有效处理天空域,保持色彩和细节信息,在算法实时性上有明显优势.     

基于均值不等关系优化的自适应图像去雾算法

针对暗通道先验去雾算法的不足,如天空区域透射率估计过小和在景深突变处易发生光晕效应,该文提出一种新颖且高效的去雾算法。首先通过几何分析建立雾图对应无雾图像暗通道图的平面扇形模型,然后设定一种新型的高斯均值函数,对其标准差进行自适应处理,用以估计扇形模型的上下边界值,通过引入均值不等关系对两侧边界进行逼近,拟合出最优无雾图像暗通道图,进一步求得最佳透射率,同时也改进局部大气光的探索方法并复原出最终结果。实验表明,与其它一些经典算法相比较,所提算法能广泛适用于各类图像,去雾程度彻底且效果清晰自然,具有较低的时间复杂度,有利于实时处理。     

图像雾霾等级评价及去雾技术研究进展

图像去雾技术是对有雾图像进行清晰化处理的一门技术,该技术的任务是去除环境因素对图像质量的影响,从而增强图像的视见度。文章首先对雾霾图像的性质与分类研究进行了论述,并进一步综述了图像去雾技术的国内外研究现状,对直方图均衡化算法,Retinex算法和先验暗通道算法等典型的去雾方法的效果进行评价。总结了各类算法的性能,分析他们各自的优势和不足。最后指出了图像去雾技术的发展趋势和未来展望。     

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.     

Aethra-net: Single image and video dehazing using autoencoder

A fast and efficient video dehazing system with low computational complexity has a huge demand among drivers during hazy winter nights. There are only a few video dehazing models that exist in literature. Video dehazing requires the sequential extraction and processing of frames. The processed frames must be restored in the same sequence as the original video. However, the existing video dehazing algorithms suffer from color distortion due to the continuous processing of frames. They are not suitable for videos with dense haze. Furthermore, some dehazing systems require hardware, whereas the proposed model is completely software-based to reduce the computational costs. In this paper, an image and video dehazing system called Aethra-Net is developed. A gush enhancer-based autoencoder is modified to obtain the transmission map. The structure of gush enhancement module resembles the processing of light entering the human eye from different paths. The multiple blocks of Resnet-101 layers are employed to overcome vanishing gradient problem. The vessel enhancement filter is also incorporated to enhance the performance of the proposed system. The proposed model has a susceptibility to compute the dehazed images effectively. The proposed model is evaluated on various benchmark datasets and compared with the existing dehazing techniques. Experimental results reveal that the performance of Aethra-Net is found superior as compared to the existing dehazing models.     

基于迁移学习的三子网图像去雾方法

目前大部分图像去雾算法只在一种或几种均匀雾图数据集中有较好的表现,对于不同风格或非均匀雾图数据集去雾效果较差,同时算法在实际应用中会因模型泛化能力差导致模型场景受限。针对上述情况,该文提出一种基于迁移学习的卷积神经网络(CNN)用于解决去雾算法中非均匀雾图处理效果不佳和模型泛化能力差等问题。首先,该文使用ImageNet预训练的模型参数作为迁移学习模型的初始参数,以加速模型训练收敛速度。其次,主干网络模型由3个子网组成：残差特征子网络、局部特征提取子网络和整体特征提取子网络。3子网结合以保证模型可从整体和局部两个方面进行特征提取,在现实雾场景(浓雾、非均匀雾)中获得较好的去雾效果。该文在模型训练效率、去雾质量和雾图场景选择灵活性3个方面进行了研究和改进,为衡量模型性能,模型选择在去雾难度较大的非均匀雾图数据集NTIRE2020和NTIRE2021上进行定量与定性实验。实验结果证明3子网模型在图像主观和客观评价指标两个方面都取得了较好的效果。该文模型改善了算法泛化性能差和小数据集难以进行模型训练的问题,可将该文成果广泛应用于小规模数据集和多变场景图像的去雾工作中。     

Image dehazing using morphological opening,dilation and Gaussian filtering

Image pre-processing is a critical stage in computer vision systems, with greater relevance when the input images are captured in outdoor environments because the pictures could contain low contrast and modified colors. A common condition present in outdoor images is haze. In this work, a new dehazing algorithm based on dark channel prior mathematical morphology operations (opening and dilation), and a Gaussian filter, is proposed. Moreover, the proposed algorithm performance is compared qualitatively and quantitatively against previously reported algorithms. Obtained results show that the proposed algorithm requires less processing time providing higher quality dehazing results than other state-of-the-art approaches.     

基于天空区域分割与置信度图导向融合的去雾方法研究

基于暗通道先验的去雾算法总是存在复原结果中天空区域处理不佳等问题,为了进一步优化对传输函数的估计,本文提出一种基于置信度图导向融合的传输函数优化方法。首先,将雾天图像的天空区域分离出来,以达到对天空区域的优化;计算窗口级暗通道与像素级暗通道,以平滑传输函数在物体边缘并保留小于窗口尺寸的细节特征;最后,计算窗口级暗通道与像素级暗通道之间的置信度图,以其为导向对两者进行融合得到优化的传输函数图,实现图像去雾。实验结果表明,本文算法可达到很好的复原结果优化效果。     

Single image dehazing using improved cycleGAN

Haze is an aggregation of very fine, widely dispersed, solid and/or liquid particles suspended in the atmosphere. In this paper, we propose an end-to-end network for single image dehazing, which enhances the CycleGAN model by introducing a transformer architecture within the generator, which is specific for haze removal. The proposed model is trained in an unpaired fashion with clear and hazy images altogether and does not require pairs of hazy and corresponding ground-truth clear images. Furthermore, the proposed model does not depend on estimating the parameters of the atmospheric scattering model. Rather, it uses a K-estimation module as the generator’s transformer for complete end-to-end modeling. The feature transformer introduced in the proposed generator model transforms the encoded features into desired feature space and then feeds them into the CycleGAN decoder to create a clear image. In the proposed model we further modified the cycle consistency loss to include the SSIM loss along with pixel-wise mean loss to produce a new loss function specific for the reconstruction task, which enhances the performance of the proposed model. The model performs well even on the high-resolution images provided in the NTIRE 2019 challenge dataset for single image dehazing. Further, we perform experiments on NYU-Depth and reside beta datasets. Results of our experiments show the efficacy of the proposed approach compared to the state-of-the-art in removing the haze from the input image.     

基于暗通道先验原理的偏振图像去雾增强算法研究

在装备试验与测试中,常规光学成像系统极易受气象环境(如雾霾、沙尘等)影响,导致探测距离、成像效果、测量精度等受到大幅限制,从而严重影响目标成像效果及关键参数获取。如何增强雾霾条件下光学探测识别能力及成像质量,成为了当前急需解决的关键问题。本文利用偏振成像优势,结合暗通道先验原理,提出了基于暗通道先验原理的偏振图像去雾增强算法。该算法首先利用采集到的偏振图像提取偏振特征,计算偏振度和偏振角;同时,采用基于区域增长算法自动提取出天空区域,对天空区域进行大气光参数估计,获取大气光偏振度及偏振角相关参数估计;然后,结合暗通道先验原理,获取无穷远处大气光强,进而计算各像素点的大气光强;最后,建立在大气物理退化模型基础上,实现图像去雾增强。实例分析与验证中,通过主观评价与客观评价两种方法,对比本文提出的方法和常见其他方法,实际结果表明,本文算法去雾增强能力较强,能有效提升光学系统的探测识别能力及成像质量,对雾霾条件下武器装备关键参数获取具有重要意义。     

暗原色先验自适应图像去雾方法

针对传统暗原色先验去雾算法存在的亮区域色彩失真、去雾参 数人工设定等问题,提出了一种基于暗原色先验改进的自适应图像去雾方法。首先,提出快 速OSTU法对雾霾图像亮暗区域进行自适应分割,并分区域获取亮暗区域的暗原色值;其次, 根据亮区域分布情况,对不同区域大气光强进行自适应估计;接着,通过分析雾霾图像直方 图特征,提出采用灰度集中度法自适应计算去雾系数;然后,运用色阶自适应调整方法进行 输出图像的色彩调整;最后,通过开展对比实验,验证了本文算法的优越性。主客观 评价结果表明:本文方法无需人为设定去雾参数,具有较好的 鲁棒性,可适用于多种浓度、 各种场景雾霾图像的去雾处理,获取的图像清晰、色彩自然,对比度高。     

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.