Despite the recent progress in image dehazing , several problems remain largely unsolved such as robustness for varying scenes , the visual quality of reconstructed images , and effectiveness and flexibility for applications . To tackle these problems , we propose a new deep network architecture for single image dehazing called DR-Net . One of the most common conditions is haze . Image dehazing , especially single image dehazing is a challenging topic since it ’s an ill-posed problem and needs to rely on extra information or prior . In this paper , we discussed the shortcomings of existing algorithms and proposed a novel step called Local Adaptive Template . Then , the new degradation model is integrated into the group-based sparse representation framework . Finally , the single image dehazing problem is regarded as an image restoration problem , which can be well optimized by GSR . The method is compared with several well-known algorithms from the literature using qualitative and quantitative evaluations , and results indicate considerable improvement over existing algorithms . We propose an effective iteration algorithm with deep CNNs to learn haze-relevant priors for image dehazing . We formulate the image dehazing problem as the minimization of a variational model with favorable data fidelity terms and prior terms to regularize the model . We solve the variational model based on the classical gradient descent method with built-in deep CNNs so that iteration-wise image priors for the atmospheric light , transmission map and clear image can be well estimated . With the development of computer vision technology and its application in intelligent transportation , military and security monitoring and other fields , image dehazing processing is an important issue and research focus in the field of computer vision . Images acquired in bad weather , such as fog and haze , are seriously degraded by the scatting of the atmosphere . However , state-of-the-art image dehazing algorithms often suffer from undesirable quantization artifacts and noises in heavily hazy regions or sky patches of hazy image where dense scattering often occurs , so that dehazed results may have poor image quality or even lose the original spectral or structural information . To address this problem , we propose a perception oriented transmission estimation method for high quality image dehazing . As the key contribution , a novel transmission model is firstly developed by posing image dehazing as a local contrast optimization problem . Besides , most existing methods suffer from noise and artifacts amplification in dense haze region after dehazing . To address these challenges , we propose a transmission adaptive regularized image recovery method for high quality single image dehazing . An initial transmission map is first obtained by a boundary constraint on the haze model . However , in the conventional training , the small-sized training image patches could not provide spatial information to the training networks when they are relatively very small compared to the original training image resolutions . In this paper , we propose a simple but effective network for high-resolution image dehazing using a conditional generative adversarial network ( CGAN ) , which is called DeHazing GAN ( DHGAN ) , where the hazy patches of scale-reduced training input images are applied to the generator network of DHGAN . By doing so , DHGAN can capture more global features of the haziness in the training image patches , thus leading to improved dehazing performance . Furthermore , comparison study demonstrates that the proposed scheme is superior to some sophisticated methods in terms of the visibility and contrast . We believe this scheme is beneficial in the image dehazing applications , especially for real-time applications . The method of single image-based dehazing is addressed in the last two decades due to its extreme variating properties in different environments . Different factors make the image dehazing process cumbersome like unbalanced airlight , contrast , and darkness in hazy images . Many estimating and learning-based techniques are used to dehaze the images to overcome the aforementioned problems that suffer from halo artifacts and weak edges . This paper presents an overview of the existing achievements on the image dehazing technique . The intent of this paper is not to review all the relevant works that have appeared in the literature , but rather to focus on two main works , that is , image dehazing scheme based on atmospheric veil and image dehazing scheme based on dark channel prior . After the overview and a comparative study , we propose an improved image dehazing method , which is based on two image dehazing schemes mentioned above . First , a max-pooling-based strategy is developed to estimate the heterogeneous atmospheric light , which can accurately describe how the amount of scattering from one or multiple point light sources is spatial-variant . And then , an efficient adaptive filtering approach is introduced to refine the medium transmission map and suppress the annoying halo artifacts produced during the process of dehazing . Finally , based on the heterogeneous atmospheric scattering model , a photorealistic haze-free image can be recovered with the estimated atmospheric light and the refined medium transmission map . The proposed approach performed the first stage in dehazing within the dark channel domain through implementation with an adaptive filter . This novel approach allowed for the dark channel features to be efficiently refined and boosted , a scheme , which according to the obtained results , significantly improved dehazing results in later stages . Experimental results showed that this approach did little to trade-off dehazing speed for efficiency . The dehazing of images shot in fog is a hot spot in the study of computer vision . Unlike dehazing methods , which use an atmospheric scattering model , the method proposed here is based on fusion coding of contours and colors . This paper proposes an effective framework to enhance the lighting levels of underwater images , increasing the number of visible , meaningful features . The process involves the dehazing of images using a dark channel prior and fast guided filters . We propose a new quad-tree method to estimate the atmospheric light . In video dehazing stage , we keep the atmospheric light unchanged in the same scene by a simple but efficient parameter , which describes the similarity of the interframe image content . By using this method , unexpected flickers are effectively eliminated . We quantitatively evaluate the proposed method on synthetic datasets and provide an extensive ablation study . Experimental results demonstrate that our approach outperforms the recent state-of-the-art methods on both dehazing and stereo matching tasks . We introduce a new database to promote visibility enhancement techniques intended for spectral image dehazing . SHIA ( Spectral Hazy Image database for Assessment ) is composed of two real indoor scenes M1 and M2 of 10 levels of fog each and their corresponding fog-free ( ground-truth ) images , taken in the visible and the near infrared ranges every 10 nm starting from 450 to 1000 nm . Such degraded images can not be used to perform robust image matching . Therefore , dehazing of hazy images is necessary . Here a contrast based dehazing technique which is based on koschmieder 's law is used to effectively dehaze hazy pair of images . In particular , a machine learning framework is adopted to obtain an accurate transmission map and several post-processing methods are used for further refinement . Experimental results show that the proposed NIR dehazing algorithm outperforms the conventional color image dehazing method for NIR image dehazing . In this paper , we instigate a single image dehazing approach which enhances the visibility of the hazy images . The proposed method focus on contrast based single image dehazing . This approach makes use of white balancing , which eliminates the color cast that is caused by the atmospheric color . In this paper , we propose a new single image dehazing method using a Multi-Scale Retinex with Color Restoration algorithm . The overall dehazing process involves three parts , the atmospheric light value calculation , transmission map estimation , and optimization after dehazing . In addition to a subjective evaluation , image quality was also evaluated objectively using two indicators , Average Gradient , and Information Entropy , in order to consider the detail of the image texture , and image dynamic range . State-of-the-art single image dehazing algorithms have some challenges to deal with images captured under complex weather conditions because their assumptions usually do not hold in those situations . In this paper , we develop a deep transmission network for robust single image dehazing . This deep transmission network simultaneously copes with three color channels and local patch information to automatically explore and exploit haze-relevant features in a learning framework . Single image dehazing is a challenging problem that aims to recover a high-quality haze-free image from a hazy image . In this paper , we propose an U-Net like deep network with contiguous memory residual blocks and gated fusion sub-network module to deal with the single image dehazing problem . Fast single image dehazing has been a challenging problem in many fields , such as computer vision and real-time applications . The existing image dehazing algorithms can not achieve a trade-off between the dehazing performance and the computational complexity . Fast single image dehazing has been a challenging problem in many fields , such as computer vision and real-time applications . Recently , many dehazing algorithms have been proposed based on the dark channel prior ( DCP ) . In this paper , we present an end-to-end network , called Cycle-Dehaze , for single image dehazing problem , which does not require pairs of hazy and corresponding ground truth images for training . That is , we train the network by feeding clean and hazy images in an unpaired manner . In the single-image dehazing problem , it is critical that the transmission is accurately estimated . However , the extracted transmission in the dark channel model can not effectively deal with the edge and the sky area because of the poor applicability of the dark channel prior to these areas . The ASM is based on transmission and atmospheric light . Thus , accurate estimation of transmission is essential for quality single image dehazing . Single image dehazing is of prime focus in research nowadays . Several methods exist that try to remove haze from a single image but they lack in the support for why a particular method was chosen . The idea of using color ellipsoids is presented in this paper for the purpose of developing a new framework for analyzing single image dehazing methods . Synthetic and real world images are used to support important properties of the color ellipsoids that give a queue to the amount of haze in an image . Abstract In this paper , a novel air light recovery method based on color-plane model is proposed . The method aims to improve the robustness of air light estimation for single image dehazing applications . Traditional methods estimating air light rely on user input or dense haze region . So the dehazed images do not often have good performance in the visual quality of local details . This paper proposes a new single-image dehazing solution based on the adaptive structure decomposition integrated multi-exposure image fusion ( PADMEF ) . A set of underexposed image sequences are extracted from a single blurred image first by a series of gamma correction and the spatial linear adjustment of saturation .
Despite the recent progress in image dehazing , several problems remain largely unsolved such as robustness for varying scenes , the visual quality of reconstructed images , and effectiveness and flexibility for applications . To tackle these problems , we propose a new deep network architecture for single image dehazing called DR-Net .
One of the most common conditions is haze . Image dehazing , especially single image dehazing is a challenging topic since it ’s an ill-posed problem and needs to rely on extra information or prior . In this paper , we discussed the shortcomings of existing algorithms and proposed a novel step called Local Adaptive Template .
Then , the new degradation model is integrated into the group-based sparse representation framework . Finally , the single image dehazing problem is regarded as an image restoration problem , which can be well optimized by GSR . The method is compared with several well-known algorithms from the literature using qualitative and quantitative evaluations , and results indicate considerable improvement over existing algorithms .
We propose an effective iteration algorithm with deep CNNs to learn haze-relevant priors for image dehazing . We formulate the image dehazing problem as the minimization of a variational model with favorable data fidelity terms and prior terms to regularize the model . We solve the variational model based on the classical gradient descent method with built-in deep CNNs so that iteration-wise image priors for the atmospheric light , transmission map and clear image can be well estimated .
With the development of computer vision technology and its application in intelligent transportation , military and security monitoring and other fields , image dehazing processing is an important issue and research focus in the field of computer vision . Images acquired in bad weather , such as fog and haze , are seriously degraded by the scatting of the atmosphere .
However , state-of-the-art image dehazing algorithms often suffer from undesirable quantization artifacts and noises in heavily hazy regions or sky patches of hazy image where dense scattering often occurs , so that dehazed results may have poor image quality or even lose the original spectral or structural information . To address this problem , we propose a perception oriented transmission estimation method for high quality image dehazing . As the key contribution , a novel transmission model is firstly developed by posing image dehazing as a local contrast optimization problem .
Besides , most existing methods suffer from noise and artifacts amplification in dense haze region after dehazing . To address these challenges , we propose a transmission adaptive regularized image recovery method for high quality single image dehazing . An initial transmission map is first obtained by a boundary constraint on the haze model .
However , in the conventional training , the small-sized training image patches could not provide spatial information to the training networks when they are relatively very small compared to the original training image resolutions . In this paper , we propose a simple but effective network for high-resolution image dehazing using a conditional generative adversarial network ( CGAN ) , which is called DeHazing GAN ( DHGAN ) , where the hazy patches of scale-reduced training input images are applied to the generator network of DHGAN . By doing so , DHGAN can capture more global features of the haziness in the training image patches , thus leading to improved dehazing performance .
Furthermore , comparison study demonstrates that the proposed scheme is superior to some sophisticated methods in terms of the visibility and contrast . We believe this scheme is beneficial in the image dehazing applications , especially for real-time applications .
The method of single image-based dehazing is addressed in the last two decades due to its extreme variating properties in different environments . Different factors make the image dehazing process cumbersome like unbalanced airlight , contrast , and darkness in hazy images . Many estimating and learning-based techniques are used to dehaze the images to overcome the aforementioned problems that suffer from halo artifacts and weak edges .
This paper presents an overview of the existing achievements on the image dehazing technique . The intent of this paper is not to review all the relevant works that have appeared in the literature , but rather to focus on two main works , that is , image dehazing scheme based on atmospheric veil and image dehazing scheme based on dark channel prior . After the overview and a comparative study , we propose an improved image dehazing method , which is based on two image dehazing schemes mentioned above .
First , a max-pooling-based strategy is developed to estimate the heterogeneous atmospheric light , which can accurately describe how the amount of scattering from one or multiple point light sources is spatial-variant . And then , an efficient adaptive filtering approach is introduced to refine the medium transmission map and suppress the annoying halo artifacts produced during the process of dehazing . Finally , based on the heterogeneous atmospheric scattering model , a photorealistic haze-free image can be recovered with the estimated atmospheric light and the refined medium transmission map .
The proposed approach performed the first stage in dehazing within the dark channel domain through implementation with an adaptive filter . This novel approach allowed for the dark channel features to be efficiently refined and boosted , a scheme , which according to the obtained results , significantly improved dehazing results in later stages . Experimental results showed that this approach did little to trade-off dehazing speed for efficiency .
The dehazing of images shot in fog is a hot spot in the study of computer vision . Unlike dehazing methods , which use an atmospheric scattering model , the method proposed here is based on fusion coding of contours and colors .
This paper proposes an effective framework to enhance the lighting levels of underwater images , increasing the number of visible , meaningful features . The process involves the dehazing of images using a dark channel prior and fast guided filters .
We propose a new quad-tree method to estimate the atmospheric light . In video dehazing stage , we keep the atmospheric light unchanged in the same scene by a simple but efficient parameter , which describes the similarity of the interframe image content . By using this method , unexpected flickers are effectively eliminated .
We quantitatively evaluate the proposed method on synthetic datasets and provide an extensive ablation study . Experimental results demonstrate that our approach outperforms the recent state-of-the-art methods on both dehazing and stereo matching tasks .
We introduce a new database to promote visibility enhancement techniques intended for spectral image dehazing . SHIA ( Spectral Hazy Image database for Assessment ) is composed of two real indoor scenes M1 and M2 of 10 levels of fog each and their corresponding fog-free ( ground-truth ) images , taken in the visible and the near infrared ranges every 10 nm starting from 450 to 1000 nm .
Such degraded images can not be used to perform robust image matching . Therefore , dehazing of hazy images is necessary . Here a contrast based dehazing technique which is based on koschmieder 's law is used to effectively dehaze hazy pair of images .
In particular , a machine learning framework is adopted to obtain an accurate transmission map and several post-processing methods are used for further refinement . Experimental results show that the proposed NIR dehazing algorithm outperforms the conventional color image dehazing method for NIR image dehazing .
In this paper , we instigate a single image dehazing approach which enhances the visibility of the hazy images . The proposed method focus on contrast based single image dehazing . This approach makes use of white balancing , which eliminates the color cast that is caused by the atmospheric color .
In this paper , we propose a new single image dehazing method using a Multi-Scale Retinex with Color Restoration algorithm . The overall dehazing process involves three parts , the atmospheric light value calculation , transmission map estimation , and optimization after dehazing . In addition to a subjective evaluation , image quality was also evaluated objectively using two indicators , Average Gradient , and Information Entropy , in order to consider the detail of the image texture , and image dynamic range .
State-of-the-art single image dehazing algorithms have some challenges to deal with images captured under complex weather conditions because their assumptions usually do not hold in those situations . In this paper , we develop a deep transmission network for robust single image dehazing . This deep transmission network simultaneously copes with three color channels and local patch information to automatically explore and exploit haze-relevant features in a learning framework .
Single image dehazing is a challenging problem that aims to recover a high-quality haze-free image from a hazy image . In this paper , we propose an U-Net like deep network with contiguous memory residual blocks and gated fusion sub-network module to deal with the single image dehazing problem .
Fast single image dehazing has been a challenging problem in many fields , such as computer vision and real-time applications . The existing image dehazing algorithms can not achieve a trade-off between the dehazing performance and the computational complexity .
Fast single image dehazing has been a challenging problem in many fields , such as computer vision and real-time applications . Recently , many dehazing algorithms have been proposed based on the dark channel prior ( DCP ) .
In this paper , we present an end-to-end network , called Cycle-Dehaze , for single image dehazing problem , which does not require pairs of hazy and corresponding ground truth images for training . That is , we train the network by feeding clean and hazy images in an unpaired manner .
In the single-image dehazing problem , it is critical that the transmission is accurately estimated . However , the extracted transmission in the dark channel model can not effectively deal with the edge and the sky area because of the poor applicability of the dark channel prior to these areas .
The ASM is based on transmission and atmospheric light . Thus , accurate estimation of transmission is essential for quality single image dehazing . Single image dehazing is of prime focus in research nowadays .
Several methods exist that try to remove haze from a single image but they lack in the support for why a particular method was chosen . The idea of using color ellipsoids is presented in this paper for the purpose of developing a new framework for analyzing single image dehazing methods . Synthetic and real world images are used to support important properties of the color ellipsoids that give a queue to the amount of haze in an image .
Abstract In this paper , a novel air light recovery method based on color-plane model is proposed . The method aims to improve the robustness of air light estimation for single image dehazing applications . Traditional methods estimating air light rely on user input or dense haze region .
So the dehazed images do not often have good performance in the visual quality of local details . This paper proposes a new single-image dehazing solution based on the adaptive structure decomposition integrated multi-exposure image fusion ( PADMEF ) . A set of underexposed image sequences are extracted from a single blurred image first by a series of gamma correction and the spatial linear adjustment of saturation .
DR-Net: Transmission Steered Single Image Dehazing Network with Weakly Supervised Refinement A Dehazing Algorithm Based on Local Adaptive Template for Transmission Estimation and Refinement Image Dehazing Using Degradation Model and Group-Based Sparse Representation Learning Deep Priors for Image Dehazing Research Reviewed on Single Image Dehazing Perception oriented transmission estimation for high quality image dehazing Single Image Dehazing With Depth-Aware Non-Local Total Variation Regularization High-Resolution Image Dehazing with Respect to Training Losses and Receptive Field Sizes A robust haze-removal scheme in polarimetric dehazing imaging based on automatic identification of sky region Single Image Dehazing and Edge Preservation Based on the Dark Channel Probability-Weighted Moments Single Image Dehazing Using Dark Channel Prior and Minimal Atmospheric Veil Single image dehazing using heterogeneous atmospheric light estimation A Novel Dark-Channel Dehazing Algorithm Based on Adaptive-Filter Enhanced SSR Theory Image-Dehazing Method Based on the Fusion Coding of Contours and Colors Enhancement of Low-Lighting Underwater Images Using Dark Channel Prior and Fast Guided Filters Fast Video Dehazing Using Per-Pixel Minimum Adjustment Deep Network for Simultaneous Stereo Matching and Dehazing A Spectral Hazy Image Database Robust Image Dehazing and Matching Based on Koschmieder s Law And SIFT Descriptor Image Dehazing Algorithm Using Near-infrared Image Characteristics Single image haze removal using white balancing and saliency map A new single image dehazing method with MSRCR algorithm Learning deep transmission network for single image dehazing Gated Contiguous Memory U-Net for Single Image Dehazing Single image haze removal algorithm using pixel-based airlight constraints A novel fast haze removal technique for single image using image pyramid Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing Fast smoothing technique with edge preservation for single image dehazing Adaptive dehazing control factor based fast single image dehazing Hazy image modeling using color ellipsoids Atmospheric light estimation in hazy images based on color-plane model Image Dehazing by an Artificial Image Fusion Method Based on Adaptive Structure Decomposition
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