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.     

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。     

DCA-CycleGAN: Unsupervised single image dehazing using Dark Channel Attention optimized CycleGAN

Single image dehazing has great significance in computer vision. In this paper, we propose a novel unsupervised Dark Channel Attention optimized CycleGAN (DCA-CycleGAN) to deal with the challenging scene with uneven and dense haze concentration. Firstly, the DCA-CycleGAN adopts the dark channel as input and then generate attention through a DCA subnetwork to handle the nonhomogeneous haze. Secondly, in addition to the conventional global discriminator, we also leverage two local discriminators to enhance the dehazing performance on the local dense haze, and a new local adversarial loss calculated strategy is been proposed. Specifically, the dehazing generator consists of two subnetworks: an auto-encoder and a dark channel attention subnetwork. The auto-encoder consists of an encoder, a feature transformation module, and a decoder. The dark channel attention subnetwork has the same structure as the encoder and the feature transformation module to ensure the same receptive field, which utilizes the dark channel to generate attention map and fine-tune the auto-encoder. Experimental results against several state-of-the-art methods demonstrate that our method can generate better visual effects, and is effective.     

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。     

人工光源条件下夜间雾天图像建模及去雾

夜间有雾图像光照不均匀,整体亮度较低,色偏严重,且人工光源周围存在光晕。现有的去雾模型和算法大多针对白天图像,其并不适用于夜间场景,夜间图像去雾颇具挑战性。该文深入分析夜间有雾图像的成像规律,建立含有人工光源的夜间雾天图像成像新模型,并在此基础上提出夜间图像去雾新算法。针对夜间图像光照不均问题,提出基于低通滤波的环境光估计方法,利用估计出的环境光可准确预测夜间场景传输率;针对目前夜间图像去雾后存在光源光晕问题,提出根据图像色度估计场景点属于近光源区域的程度,使算法能自适应地处理光源区域和非光源区域;针对非一致色偏问题,利用直方图匹配方法进行颜色校正。对大量图像进行实验,并与现有白天、夜晚图像去雾算法进行比较,验证了该文提出的夜间雾天图像成像模型及去雾算法的有效性。     

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。     

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.     

From synthetic to natural — single natural image dehazing deep networks using synthetic dataset domain randomization

Image dehazing methods aim to solve the problem of poor visibility in images due to haze. Techniques proposed for image dehazing in literature focus on image priors, haze lines or data driven statistical models. Variations of the classical methods relying on prior model or haze line model use no-reference image quality metrics to prove their dehazing performance. Recently developed deep learning models rely on huge amounts of hazy, haze-free pairs for training, and uses PSNR and SSIM like image reconstruction metrics to show their performance. These methods perform poorly on no-reference image quality assessments and also dehazes poorly at the depths of the image. These methods though can be optimized for memory usage and are faster. This work presents a deep learning model (Feature Fusion Attention Network) trained on a domain randomized synthetic dataset generated in simulation. The proposed model achieves the highest scores on blind image assessments through the gradient rationing technique for a deep learning-based approach by a significant margin. The images were evaluated on full-reference metrics as well and obtained favorable results. This approach also yields one of the highest edge sharpness obtained after dehazing. The training procedure adopted to obtain significant gains on real-world dehazing, without using any real-world data is also detailed in this paper.     

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.     

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.     

Densely connected convolutional transformer for single image dehazing

Image Dehazing is an important low-level vision task that aims to remove the haze from an image. In this paper, we proposed Densely Connected Convolutional Transformer (DCCT) for single image dehazing. DCCT is an efficient architecture that combines the multi-head Performer with the local dependencies. To prevent loss of information between features at different levels, we propose a learnable connection layer that is used to fuse features at different levels across the entire architecture. We guide the training of DCCT through a joint loss considering a supervised metric learning approach that allows us to consider both negative and positive features for a multi-image perceptual loss. We validate the design choices and the effectiveness of the proposed DCCT through ablation studies. Through comparison with the representative techniques, we establish that the proposed DCCT is highly competitive with the state of the art.     

Attention mechanism enhancement algorithm based on cycle consistent generative adversarial networks for single image dehazing

This paper proposes AMEA-GAN, an attention mechanism enhancement algorithm. It is cycle consistency-based generative adversarial networks for single image dehazing, which follows the mechanism of the human retina and to a great extent guarantees the color authenticity of enhanced images. To address the color distortion and fog artifacts in real-world images caused by most image dehazing methods, we refer to the human visual neurons and use the attention mechanism of similar Horizontal cell and Amazon cell in the retina to improve the structure of the generator adversarial networks. By introducing our proposed attention mechanism, the effect of haze removal becomes more natural without leaving any artifacts, especially in the dense fog area. We also use an improved symmetrical structure of FUNIE-GAN to improve the visual color perception or the color authenticity of the enhanced image and to produce a better visual effect. Experimental results show that our proposed model generates satisfactory results, that is, the output image of AMEA-GAN bears a strong sense of reality. Compared with state-of-the-art methods, AMEA-GAN not only dehazes images taken in daytime scenes but also can enhance images taken in nighttime scenes and even optical remote sensing imagery.     

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。     

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。     

动态大气散射系数的颜色衰减先验图像去雾

针对颜色衰减先验图像去雾算法存在对较浓的有雾图像去雾效果不佳的问题,提出基于动态大气散射系数的颜色衰减先验图像去雾算法.用动态大气散射系数取代颜色衰减先验去雾算法中恒定大气散射系数的假设,定义大气散射系数为关于图像景深的指数函数.利用Middlebury stereo datasets中无雾图像和相应的景深图像得到合成有雾图像.采用均方误差(MSE)和结构相似度(SSIM)的综合评价参数MSE-SSIM确定上述指数函数的两个参数的最佳取值.实验结果表明与颜色衰减先验算法、He、Meng算法相比,该算法的去雾图像清晰颜色自然,有效地提高了去雾效果.     

A survey on analysis and implementation of state-of-the-art haze removal techniques

Haze is a poor-quality state described by the opalescent appearance of the atmosphere which reduces the visibility. It is caused by high concentrations of atmospheric air pollutants, such as dust, smoke and other particles that scatter and absorb sunlight. The poor visibility can result in the failure of multiple computer vision applications such as smart transport systems, image processing, object detection, surveillance etc. One of the major issues in the field of image processing is the restoration of images that are corrupted due to different degradations. Typically, the images or videos captured in the outside environment have low contrast, colour fade and restricted visibility due to suspended particles of the atmosphere that directly influence the image quality. This can cause difficulty in identifying the objects in the captured hazy images or frames. To address this problem, several image dehazing techniques have been developed in the literature, each of which has its own advantages and limitations, but effective image restoration remains a challenging task. In recent times, various learning (Machine learning & Deep learning) based methods greatly condensed the drawbacks of manual design of haze related features and reduces the difficulty in efficient restoration of images with less computational time and cost. The current state-of-the-art methods for haze free images, mainly from the last decade, are thoroughly examined in this survey. Moreover, this paper systematically summarizes the hardware implementations of various haze removal methods in real time. It is with the hope that this current survey acts as a reference for researchers in this scientific area and to provide a direction for future improvements based on current achievements.     

An improved Gamma correction model for image dehazing in a multi-exposure fusion framework

A usual problem encountered during bad weather conditions is the degraded image quality due to haze/fog. In basic Gamma correction method there is always an uncertainty regarding the choice of a particular exponential factor, which improves the quality of the input image because of the nonlinearity involved in the process. This issue has been solved in this study by proposing a modified Gamma correction method, in which the exponential correction factor is varied incrementally to generate images. We also propose the implementation of an automatic image selection criterion for fusion which helps chose images with varied and distinct features. The implementation of the multi-exposure fusion framework is done in the hue-saturation-value color space which has close resemblance with the human vision. The intensity channel of the selected images is fused in the gradient domain which captures minute details and takes an edge as compared to other conventional fusion based methods. The fused saturation channel is obtained by averaging fusion followed by enhancement using a non-linear sigmoid function. The hue channel of the input hazy image is left unprocessed to avoid color distortion. The experimental analysis demonstrates that the proposed method outperforms most of the single image dehazing methods.     

一种联合雾线和凸优化的单幅图像去雾算法

为解决传统去雾算法容易在天空区域出现光晕效应和复原后的图像颜色过饱和等问题,提出了一种联合雾线和凸优化的单幅图像去雾算法。该算法使用雾线先验来估计大气光值,并通过离散小波变换构建了一个降维的子带雾图模型,进一步将双线性耦合项和大气光传输分布作为线性优化变量进行凸优化求解来得到透射率,最后通过大气散射模型恢复出无雾图像。实验结果表明该算法在大多数情况下恢复的图像清晰自然,与其他几种常用的图像去雾算法的客观对比,也证实了该算法的可行性和有效性。     

Single underwater image haze removal with a learning-based approach to blurriness estimation

Underwater images are usually degraded due to light scattering and absorption. To recover the scene radiance of degraded underwater images, a new haze removal method is presented by incorporating a learning-based approach to blurriness estimation with the image formation model. Firstly, the image blurriness is estimated with a linear model trained on a set of selected grayscale images, the average Gaussian images and blurriness images. With the estimated image blurriness, three intermediate background lights (BLs) are computed to obtain the synthesized BL. Then the scene depth is calculated by using the estimated image blurriness and BL to construct a transmission map and restore the scene radiance. Compared with other haze removal methods, haze in degraded underwater images can be removed more accurately with our proposed method. Moreover, visual inspection, quantitative evaluation and application test demonstrate that our method is superior to the compared methods and beneficial to high-level vision tasks.