Synthetic Hazy Images Research Articles

Densely integrated multi-branch attentive net for image dehazing

Single image dehazing is a fundamental but challenging task in image processing. Various deep learning-based methods have achieved great dehazing performance. However, there are still hazy residues, even color distortion and texture loss when removing haze from complex outdoor images in dense hazy scenes. A densely integrated multi-branch attentive net for image dehazing is proposed in the paper to address the above problems. The network includes a multi-scale feature extraction module and a dense-feature fusion module. The multi-scale feature extraction module adopts a multi-branch structure composed of residual channel attention blocks, which can expand the receptive field and filter the extracted features of diverse scales by weighting for fusion. It raises network learning accuracy. The dense-feature fusion module contains a multi-level feature fusion module for front and back layers, a color information renovation module, and a feature enhancement module. It achieves dynamically adjusting the channel weights of features at diverse scales, learning rich context information and suppressing redundant information, and compensating for the lack of color and texture information. The dense-feature fusion module bolsters the generalization capability of the network. The proposed method achieves superior objective and subjective evaluation results via quantitative and qualitative experiments on synthetic hazy images and natural hazy images, with better generalization ability and dehazing effect than the current SOTA dehazing algorithms, and effectively ameliorates the color distortion and incomplete dehazing.

Photo realistic synthetic dataset and multi-scale attention dehazing network

Deep learning is a powerful tool in the realm of low-level computer vision and has achieved significant success in image dehazing. However, previous works have predominantly focused on synthetic hazy images, thereby overlooking the inherent differences between real-world hazy images and their synthetic counterparts. These prior approaches face a performance decline when models trained with synthetic hazy images are applied to naturally hazy scenes. In this context, we propose a novel method aimed at minimizing the discrepancies between real and synthetic hazy images, thus enhancing the dehazing performance for real-world scenarios. Specifically, our approach includes refining the synthesized transmission map, which often misses details around object boundaries. Furthermore, we implement a technique that transfers the visual appearance of natural haze onto simulated images. Additionally, we introduce noise into the synthesized hazy images to enhance realism. To demonstrate the effectiveness of our dataset, we introduce a multi-scale attention dehazing network, which delivers state-of-the-art dehazing results. Extensive experiments robustly attest to the superior performance of our proposed method. We further conduct experiments to validate the efficacy of the proposed dataset in addressing the challenges posed by real-world haze.

Restoration of Binocular Images Degraded by Optical Scattering through Estimation of Atmospheric Coefficients.

A binocular vision-based approach for the restoration of images captured in a scattering medium is presented. The scene depth is computed by triangulation using stereo matching. Next, the atmospheric parameters of the medium are determined with an introduced estimator based on the Monte Carlo method. Finally, image restoration is performed using an atmospheric optics model. The proposed approach effectively suppresses optical scattering effects without introducing noticeable artifacts in processed images. The accuracy of the proposed approach in the estimation of atmospheric parameters and image restoration is evaluated using synthetic hazy images constructed from a well-known database. The practical viability of our approach is also confirmed through a real experiment for depth estimation, atmospheric parameter estimation, and image restoration in a scattering medium. The results highlight the applicability of our approach in computer vision applications in challenging atmospheric conditions.

Multi-Scale Attention Feature Enhancement Network for Single Image Dehazing.

Aiming to solve the problem of color distortion and loss of detail information in most dehazing algorithms, an end-to-end image dehazing network based on multi-scale feature enhancement is proposed. Firstly, the feature extraction enhancement module is used to capture the detailed information of hazy images and expand the receptive field. Secondly, the channel attention mechanism and pixel attention mechanism of the feature fusion enhancement module are used to dynamically adjust the weights of different channels and pixels. Thirdly, the context enhancement module is used to enhance the context semantic information, suppress redundant information, and obtain the haze density image with higher detail. Finally, our method removes haze, preserves image color, and ensures image details. The proposed method achieved a PSNR score of 33.74, SSIM scores of 0.9843 and LPIPS distance of 0.0040 on the SOTS-outdoor dataset. Compared with representative dehazing methods, it demonstrates better dehazing performance and proves the advantages of the proposed method on synthetic hazy images. Combined with dehazing experiments on real hazy images, the results show that our method can effectively improve dehazing performance while preserving more image details and achieving color fidelity.

A Novel Parameter Adaptive Dual Channel MSPCNN Based Single Image Dehazing for Intelligent Transportation Systems

Visibility issues in intelligent transportation systems are exacerbated by bad weather conditions such as fog and haze. It has been observed from recent studies that major road accidents have occurred in the world due to low visibility and inclement weather conditions. Single image dehazing attempts to restore a haze-free image from an unconstrained hazy image. We proposed a dehazing method by cascading two models utilizing a novel parameter-adaptive dual-channel modified simplified pulse coupled neural network (PA-DC-MSPCNN). The first model uses a new color channel for removing haze from images. The second model is the improved brightness preserving model (I-GIHE), which retains the brightness of the image while improving the gradient strength. To integrate the results from these two models and provide a pleasing haze-free image, a PA-DC-MSPCNN-based fusion is used. Furthermore, the proposed approach is deployed on a Xilinx Zynq SoC by exploiting the recently released PYNQ platform. The dehazing system runs on a PYNQ-Z2 all-programmable SoC platform, where it will input the camera feed through the FPGA unit and carry out the dehazing algorithm in the ARM core. This configuration has allowed reaching real-time processing speed for image dehazing. The results of dehazing are analyzed using both synthetic and real-world hazy images. Synthetic hazy images are acquired from the O-HAZE, I-HAZE, SOTS, and FRIDA datasets, while real-world hazy images are taken from the RailSem19, E-TUVD dataset, and the internet. For evaluation, twelve cutting-edge approaches are chosen. The proposed method is also analyzed on underwater and low-light images. Extensive experiments indicate that the proposed method outperforms state-of-the-art methods of qualitative and quantitative performances.

Improving Semantic Segmentation Under Hazy Weather for Autonomous Vehicles Using Explainable Artificial Intelligence and Adaptive Dehazing Approach

Haze-level discriminators are highly desirable for autonomous vehicles to handle segmentation tasks successfully in hazy and foggy outdoor environments. Neural networks trained to detect clear images show more false positives and unrecognize the pixel patterns for the class categories when they encounter hazy images. Here, we propose a novel dehazing scheme called Adaptive Dehazing (AD) which can separate the unacceptable hazy images and apply the dehazing technique only to those images before passing it to the neural network for segmentation. Various thresholds have been defined to classify hazy images into four hazy categories: heavy, moderate, slight and clear. Also, a hazy image generator is implemented to create synthetic hazy images that replicate the actual hazy road scene conditions for testing the proposed algorithm. In addition, we use the Explainable Artificial Intelligence (XAI) method to understand the feature selected by different layers in the network before and after applying the AD and their contribution to improving the Intersection over Union (IoU) and pixel accuracy (PA) of the segmented categories. Extensive experiments demonstrate quantitatively as well as qualitatively the performance improvement in the segmentation task. Our AD scheme significantly outperforms the previous state-of-the-art dehazing methods.

Single Image Dehazing Using Saturation Line Prior.

Saturation information in hazy images is conducive to effective haze removal, However, existing saturation-based dehazing methods just focus on the saturation value of each pixel itself, while the higher-level distribution characteristic between pixels regarding saturation remains to be harnessed. In this paper, we observe that the pixels, which share the same surface reflectance coefficient in the local patches of haze-free images, exhibit a linear relationship between their saturation component and the reciprocal of their brightness component in the corresponding hazy images normalized by atmospheric light. Furthermore, the intercept of the line described by this linear relationship on the saturation axis is exactly the saturation value of these pixels in the haze-free images. Using this characteristic of saturation, termed saturation line prior (SLP), the transmission estimation is translated into the construction of saturation lines. Accordingly, a new dehazing framework using SLP is proposed, which employs the intrinsic relevance between pixels to achieve a reliable saturation line construction for transmission estimation. This approach can recover the fine details and attain realistic colors from hazy scenes, resulting in a remarkable visibility improvement. Extensive experiments in real-world and synthetic hazy images show that the proposed method performs favorably against state-of-the-art dehazing methods. Code is available on https://github.com/LPengYang/Saturation-Line-Prior.

HDNet: A dual-stream network with progressive fusion for image hazing detection

Image hazing refers to artificially adding haze to a clear image to produce a natural haze effect. However, it might also be used as an image forgery for malicious purposes such as misleading tourists or spreading fake weather information. Up to the present, there are still no works specially designed for fake hazy image detection. Inspired by the fact that almost all artificial hazy images are synthesized based on the atmospheric scattering model (ASM), we observe that though synthetic and real hazy images are very difficult to be differentiated by naked eyes, there still exist some ASM-related imprints in synthesized hazy images. Specifically, the subtle differences between synthetic hazy images and real hazy ones are image saturation and haze distribution. In this work, we propose a dual-stream image hazing detection network, namely HDNet, to distinguish synthetic hazy images from natural hazy ones. One stream explicitly learns features from the image saturation channel, and the other one learns haze distribution features from the dark channel and gray level co-occurrence matrix. Moreover, to fuse well the learned features from two streams, we propose a progressive attention mechanism. A stage-wise learning strategy is exploited for network training, in which the learning process is divided into four sub-tasks that are completed stage-by-stage. Extensive experimental results prove that the proposed HDNet achieves desirable detection accuracy and robustness.

TMS-GAN: A Twofold Multi-Scale Generative Adversarial Network for Single Image Dehazing

In recent years, learning-based single image dehazing networks have been comprehensively developed. However, performance improvement is limited due to domain shift between trained synthetic hazy images and untrained real-world hazy images. To alleviate this issue, this paper proposes a real-world dehazing targeted training scheme which nearly realizes paired real-world data training. As a result, a Twofold Multi-scale Generative Adversarial Network (TMS-GAN) consisting of a Haze-generation GAN (HgGAN) and a Haze-removal GAN (HrGAN) is designed. HgGAN attributes real haze properties to synthetic images and HrGAN removes haze from both synthetic and generated fake realistic data under supervision. Thus, the proposed method can better adapt to real-world image dehazing using this cooperative training scheme. Meanwhile, several structural advances of TMS-GAN also improve dehazing performance. Specifically, a haze residual map based on atmospheric scattering model is deduced in HgGAN for fake realistic data generation. The dual-branch generator in HrGAN draws attention to detail restoration by one branch along with another color-branch. A plug-and-play Multi-attention Progressive Fusion Module (MAPFM) is proposed and inserted in both HgGAN and HrGAN. MAPFM incorporates multi-attention mechanism to guide multi-scale feature fusion in a progressive manner, in which Adjacency-attention Block (AAB) can capture contributing features of each level and Self-attention Block (SAB) can establish non-local dependency of feature fusion. Experiments on mainstream benchmarks show that the proposed framework is superior especially on real-world hazy images among single image dehazing methods.

Dual-Scale Single Image Dehazing via Neural Augmentation.

Model-based single image dehazing algorithms restore haze-free images with sharp edges and rich details for real-world hazy images at the expense of low PSNR and SSIM values for synthetic hazy images. Data-driven ones restore haze-free images with high PSNR and SSIM values for synthetic hazy images but with low contrast, and even some remaining haze for real-world hazy images. In this paper, a novel single image dehazing algorithm is introduced by combining model-based and data-driven approaches. Both transmission map and atmospheric light are first estimated by the model-based methods, and then refined by dual-scale generative adversarial networks (GANs) based approaches. The resultant algorithm forms a neural augmentation which converges very fast while the corresponding data-driven approach might not converge. Haze-free images are restored by using the estimated transmission map and atmospheric light as well as the Koschmieder's law. Experimental results indicate that the proposed algorithm can remove haze well from real-world and synthetic hazy images.

A unified weighted variational model for simultaneously haze removal and noise suppression of hazy images

Visibility degradation of outdoor hazy images caused by scattering medium (e.g. haze, fog or smoke) is a challenging issue due to its ill-posed nature. Moreover, the noises are inevitably introduced to the hazy images because of various factors in real-world haze imaging. However, traditional atmospheric scattering model (ASM) fails to consider the noise term, which may lead to noise amplification after restoration. In this paper, traditional ASM is first modified by adding the noise term to make it suitable for real-world hazy images. Based on the modified model, a novel unified weighted variational model is proposed to address the problems of both haze removal and noise suppression simultaneously. Specifically, we use ℓ1 norm to enforce the piece-wise continuous on the scene radiance, adopt weighted ℓ2 norm based on exponentiated mean local variance to constrain the transmission, and apply the overall intensity constraint on the noise. An alternating optimization method is adopted to solve the proposed model. Compared with various dehazing methods, the proposed method can provide comparable or better results both qualitative and quantitative assessments on real-world and synthetic hazy images.

Hierarchical Density-Aware Dehazing Network.

The commonly used atmospheric model in image dehazing cannot hold in real cases. Although deep end-to-end networks were presented to solve this problem by disregarding the physical model, the transmission map in the atmospheric model contains significant haze density information, which cannot simply be ignored. In this article, we propose a novel hierarchical density-aware dehazing network, which consists of a the densely connected pyramid encoder, a density generator, and a Laplacian pyramid decoder. The proposed network incorporates density estimation but alleviates the constraint of the atmospheric model. The predicted haze density then guides the Laplacian pyramid decoder to generate a haze-free image in a coarse-to-fine fashion. In addition, we introduce a multiscale discriminator to preserve global and local consistency for dehazing. We conduct extensive experiments on natural and synthetic hazy images, which prove that the proposed model performs favorably against the state-of-the-art dehazing approaches.

DCNet: Dark Channel Network for single-image dehazing

Single-image dehazing is an extensively studied field and an ill-posed problem faced by vision-based systems in an outdoor environment. This paper proposes a dark channel network to estimate the transmission map of an input hazy scene for single-image dehazing. The architecture constitutes two major components—feature extraction layer and convolutional neural network layer. The former extracts the haze relevant features, while latter convolve these features with filter kernels to estimate the true scene transmission. Finally, the estimated transmission map is used to obtain the dehazed image using atmospheric scattering model. The experiments have been performed on synthetic hazy images and benchmark hazy dataset available in the literature. The performance of the proposed architecture outperforms the existing models in terms of standard quantitative metrics—mean square error, structural similarity index, and peak signal-to-noise ratio.

Distortion-free image dehazing by superpixels and ensemble neural network

Single image dehazing is a technique used to remove the effect of haze from an image captured in poor weather conditions. Due to the scattering of particles, a captured image suffers from low visibility and contrast. Besides, scattering also adds nonlinear noise to the captured image. Existing image dehazing methods improve the visibility of the hazy image. However, these methods significantly generate artifacts such as halo at the depth discontinuities, blocking, and color aliasing in the sky regions. Some methods addressed this problem, but these methods introduce other issues such as loss of details, blurring effects, and oversaturation in the dehazed image. This paper proposes a method using superpixels and ensemble nonlinear regression to estimate the transmission that improves the visibility of a hazy image without any artifact. Conventional machine learning methods require a vast amount of haze-free and hazy images of different haze concentrations to train the model. The use of superpixels offers less number of training examples and also helps in reducing halo artifacts. The ensemble nonlinear regression predicts the transmission for a superpixel in such a way that the recovered image looks more natural, especially in the sky regions. The proposed method is evaluated by the various distortion parameters on real-world challenging and synthetic hazy images. The qualitative and quantitative analysis in experimental results proves that the proposed method is superior to that of state-of-the-art dehazing methods.

Single Image Dehazing via Semi-Supervised Domain Translation and Architecture Search

Data-driven methods have demonstrated great potential in single image dehazing, most of which are trained using the hazy images synthesized by the atmospheric scattering model. However, the model cannot accurately describe the complicated degradation caused by haze. At the same time, it is impractical to obtain the pixel-to-pixel aligned clear-scene counterparts of real-world hazy images for supervised training. In this letter, we formulate dehazing as a semi-supervised domain translation problem. For better generalization, two auxiliary domain translation tasks are designed to capture the properties of real-world haze and align synthetic hazy images to real-world ones to reduce the domain gap. Dehazing and the auxiliary tasks are conducted in shared latent spaces by a unified framework, and we use differential optimization to search the architectures of the framework. We evaluate the efficacy of the proposed work using one synthetic and three real-world benchmarks that cover the challenging cases in wild scenarios, and it outperforms state-of-the-art algorithms on these benchmarks. The benefits brought by auxiliary domain translation tasks and architecture search are also verified by ablation experiments.

URNet: A U-Net based residual network for image dehazing

Low visibility in hazy weather causes the loss of image details in digital images captured by some imaging devices such as monitors. This paper proposes an end-to-end U-Net based residual network (URNet) to improve the visibility of hazy images. The encoder module of URNet uses hybrid convolution combining standard convolution with dilated convolution to expand the receptive field for extracting image features with more details. The URNet embeds several building blocks of ResNet into the junction between the encoder module and the decoder module. This prevents network performance degradation due to the vanishing gradient. After considering large absolute difference on image saturation and value components between hazy images and haze-free images in the HSV color space, the URNet defines a new loss function to better guide the network training. Experimental results on synthetic hazy images and real hazy images show that the URNet significantly improves the image dehazing effect compared to the state-of-the-art methods.

An End-to-End Network for Image De-Hazing and Beyond

Degradation in the qualityof images that are captured in the hazy environment is mainlydue to 1) different weather conditions and 2) the attenuation in reflected light. These factors introduce a severe color distortion and low visibility in the captured images. To tackle these problems, we propose an end-to-end trainable image de-hazing network named as LIGHT-Net. The proposed LIGHT-Net comprises of color constancy module and haze reduction module. Among these, the color constancy module removes the color cast added in hazy image due to the weather condition. Whereas, the proposed haze reduction module, which is build using an inception-residual block, is aimed to reduce the effect of haze as well as to improve the visibility in the hazy image. Unlike traditional feature concatenation, in the haze reduction module, we propose a dense feature sharing to effectively share the features learned at initial layers across the network. In general, a major hurdle to train a convolution neural network for haze removal task is the unavailability of large-scale real-world hazy, and corresponding haze-free image (i.e. paired data). Thus, we make use of an unpaired training approach to train the proposed LIGHT-Net for image de-hazing. Extensive analysis has been carried out to validate the necessity and impact of each sub-block of the proposed LIGHT-Net. A large set of real-world hazy images captured in different weather conditions are considered to validate the proposed approach for image de-hazing. Also, the benchmark synthetic hazy image database is considered for a quantitative analysis of the proposed LIGHTNet for image de-hazing. Further, we have shown the usefulness of the proposed LIGHT-Net for underwater image enhancement. Experiments show that the proposed LIGHT-Net outperforms the other existing approaches for both image de-hazing as well as underwater image enhancement.

HazDesNet: An End-to-End Network for Haze Density Prediction

Vision-based intelligent systems such as driver assistance systems and transportation systems should take into account weather conditions. The presence of haze in images can be a critical threat to driving scenarios. Haze density measures the visibility and usability of hazy images captured in real-world conditions. The prediction of haze density can be valuable in various vision-based intelligent systems, especially in those systems deployed in outdoor environments. Haze density prediction is a challenging task since the haze and many scene contents have a lot in common in appearance. Existing methods generally utilize different priors and design complex handcrafted features to predict the visibility or haze density of the image. In this article, we propose a novel end-to-end convolutional neural network (CNN) based method to predict haze density, named as HazDesNet. Our HazDesNet takes a hazy image as input and predicts a pixel-level haze density map. The density map is then refined and smoothed, and the average of the refined map is calculated as the global haze density of the image. To verify the performance of HazDesNet, a subjective human study is performed to build a Human Perceptual Haze Density (HPHD) database, which includes 500 real-world hazy images and 100 synthetic hazy images, and the corresponding human-rated perceptual haze density scores. Experimental results show that our method achieves the best haze density prediction performance on our built HPHD database and existing databases. Besides the global quantitative results, our HazDesNet is capable of predicting a continuous, stable, fine, and high-resolution haze density map. We will make the database and code publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/JiaheZhang/HazDesNet</uri> .

Single Image Dehazing by Predicting Atmospheric Scattering Parameters

In this work we propose a method for single image dehazing that exploits a physical model to recover the haze-free image by estimating the atmospheric scattering parameters. Cycle consistency is used to further improve the reconstruction quality of local structures and objects in the scene as well. Experimental results on four real and synthetic hazy image datasets show the effectiveness of the proposed method in terms of two commonly used full-reference image quality metrics.

Image dehazing algorithm by combining light field multi-cues and atmospheric scattering model

Image captured in foggy weather often exhibits low contrast and poor image quality, which may have a negative impact on computer vision applications. Aiming at these problems, we propose an image dehazing algorithm by combining light field technology with atmospheric scattering model. Firstly, taking the advantages of capturing multi-view information from light field camera is used to extracting defocus cues and correspondence cues, which are used to estimating the depth information of hazy images, and use the obtained depth information to calculating the scene’s initial transmission. Then use scene depth information to build a new weight function, and combined it with 1-norm context regularization to optimizing the initial transmission map iteratively. Finally, the central perspective image of hazy light field images is dehazed using atmospheric scattering model to obtain the final dehazed images. Experimental results on synthetic hazy images and real hazy images demonstrate that, compared to existing single image dehazing algorithms, the peak signal to noise ratio get 2 dB improvement and the structural similarity raise about 0.04. Moreover, our approach preserves more fine structural information of images and has faithful color fidelity, thus yielding a superior image dehazing result.