Image Dehazing Research Articles

An Optimized Stationary Wavelet Fusion Technique for Image de-hazing

Nowadays, the amount of smoke and dust in the air is increasing significantly due to industrialization. The smoke and dust particles accumulate in the relatively dry air and cause haze in the surrounding area, impairs visibility. This haze also affects photography, which reduces the images' quality and looks unnatural. The hazy atmosphere affects even pictures taken with a cell phone in everyday life. There are many methods to remove this haze content from the image, but they have not yielded great results. The long-time and short-time shots constantly differed while attempting to eliminate atmospheric haze from the images. To solve this problem, a fusion rule was proposed to fuse the luminance and dark channel prior (DCP) methods. The transmission estimated with the DCP method contributes mainly to the foreground regions, while the luminance model deals with the celestial regions. The fusion technique is a pixel-level fusion approach in the transform domain. The proposed approach combines the transmittance values obtained from the dark channel in front of the foreground region (background) and the luminance model for the sky region in the transform domain using the Stationary Wavelet Transform (SWT) with the optimized level of decomposition. The proposed algorithm was subjected to quantitative analysis of some statistical measures. The result shows that the proposed method successfully maintains the maximum visual truth content by effectively removing atmospheric haze from the images.

Density‐Aware Diffusion Model for Efficient Image Dehazing

AbstractExisting image dehazing methods have made remarkable progress. However, they generally perform poorly on images with dense haze, and often suffer from unsatisfactory results with detail degradation or color distortion. In this paper, we propose a density‐aware diffusion model (DADM) for image dehazing. Guided by the haze density, our DADM can handle images with dense haze and complex environments. Specifically, we introduce a density‐aware dehazing network (DADNet) in the reverse diffusion process, which can help DADM gradually recover a clear haze‐free image from a haze image. To improve the performance of the network, we design a cross‐feature density extraction module (CDEModule) to extract the haze density for the image and a density‐guided feature fusion block (DFFBlock) to learn the effective contextual features. Furthermore, we introduce an indirect sampling strategy in the test sampling process, which not only suppresses the accumulation of errors but also ensures the stability of the results. Extensive experiments on popular benchmarks validate the superior performance of the proposed method. The code is released in https://github.com/benchacha/DADM.

Breaking the Haze: Enhanced CycleGAN Models with Self-Attention for Single-Image Dehazing

A common preprocessing step in vision-based applications is image dehazing, which has drawbacks such color distortion and detail loss. Conventional techniques frequently fall short of sufficiently retrieving sharp images from foggy situations. In order to improve single-image dehazing, this paper presents a unique method that combines Self-Attention Generative Adversarial Networks (SAGANs) with Cycle-Consistent Generative Adversarial Networks (CycleGAN). In order to capture long-range dependencies and enhance the perceptual quality of the dehazed images, our method includes self-attention processes from SAGANs and makes use of CycleGAN's strengths for efficient image-to-image translation without the need for paired training samples. We present a dual-framework design in which SAGANs improve textural and contextual details by focusing on relevant characteristics throughout the whole image space, while CycleGAN assures content fidelity through cycle consistency. We show through our studies that this integrated strategy achieves higher scores in common metrics like Peak Signal-to-Noise Ratio (PSNR) of 34.146 dB and Structural Similarity Index (SSIM) of 0.964, greatly outperforming both qualitative and quantitative assessments of previous methods. Together with establishing a new standard for single-image dehazing, this work shows how effective hybrid generative models can be when handling challenging image processing problems.

Norm constraints pyramid for image dehazing

Dark channel prior-based methods have achieved remarkable performance for image dehazing. However, previous studies are mostly focused on the accuracy of the assumptions used in the target scenes, which incurs color distortion and brightness reduction when the models are used for real-world hazy images. We propose a norm constraints pyramid framework to improve the generalization performance of dehazing. First, a local color adaptive correction approach is devised to ascertain whether there is any color bias and thereafter rectify it automatically. Furthermore, multiple norm constraint methods are developed to improve the transmission and accomplish the first image removal. Finally, a non-linear enhancement method is created via this restriction that precisely modifies the brightness of an image. Through extensive experiments, we demonstrate that our framework establishes the new state-of- the-art performance for real-world dehazing, in terms of visual quality assessed by no-reference quality metrics as well as subjective evaluation and downstream task performance indicator.

EENet: An effective and efficient network for single image dehazing

While numerous solutions leveraging convolutional neural networks and Transformers have been proposed for image dehazing, there remains significant potential to improve the balance between efficiency and reconstruction performance. In this paper, we introduce an efficient and effective network named EENet, designed for image dehazing through enhanced spatial–spectral learning. EENet comprises three primary modules: the frequency processing module, the spatial processing module, and the dual-domain interaction module. Specifically, the frequency processing module handles Fourier components individually based on their distinct properties for image dehazing while also modeling global dependencies according to the convolution theorem. Additionally, the spatial processing module is designed to enable multi-scale learning. Finally, the dual-domain interaction module promotes information exchange between the frequency and spatial domains. Extensive experiments demonstrate that EENet achieves state-of-the-art performance on seven synthetic and real-world datasets for image dehazing. Moreover, the network’s generalization ability is validated by extending it to image desnowing, image defocus deblurring, and low-light image enhancement.

WTCL-Dehaze: Rethinking Real-World Image Dehazing via Wavelet Transform and Contrastive Learning

Images captured in hazy outdoor conditions often suffer from colour distortion, low contrast, and loss of detail, which impair high-level vision tasks. Single image dehazing is essential for applications such as autonomous driving and surveillance, with the aim of restoring image clarity. In this work, we propose WTCL-Dehaze an enhanced semi-supervised dehazing network that integrates Contrastive Loss and Discrete Wavelet Transform (DWT). We incorporate contrastive regularization to enhance feature representation by contrasting hazy and clear image pairs. Additionally, we utilize DWT for multi-scale feature extraction, effectively capturing high-frequency details and global structures. Our approach leverages both labelled and unlabelled data to mitigate the domain gap and improve generalization. The model is trained on a combination of synthetic and real-world datasets, ensuring robust performance across different scenarios. Extensive experiments demonstrate that our proposed algorithm achieves superior performance and improved robustness compared to state-of-the-art single image dehazing methods on both benchmark datasets and real-world images.

MA-MFCNet: Mixed Attention-Based Multi-Scale Feature Calibration Network for Image Dehazing

MA-MFCNet: Mixed Attention-Based Multi-Scale Feature Calibration Network for Image Dehazing

EPQ-GAN: Evolutionary Perceptual Quality Assessment Generative Adversarial Network for Image Dehazing

EPQ-GAN: Evolutionary Perceptual Quality Assessment Generative Adversarial Network for Image Dehazing

Image dehazing via self-supervised depth guidance

Self-supervised learning methods have demonstrated promising benefits to feature representation learning for image dehazing tasks, especially for avoiding the laborious work of collecting hazy-clean image pairs, while also enabling better generalization abilities of the model. Despite the long-standing interests in depth estimation for image dehazing tasks, few works have fully explored the interactions between depth and dehazing tasks in an unsupervised manner. In this paper, a self-supervised image dehazing framework under the guidance of self-supervised depth estimation has been proposed, to fully exploit the interactions between depth and hazes for image dehazing. Specifically, the hazy image and the corresponding depth estimation are generated and optimized from the clear image in a dual-network self-supervised manner. The correlations between depth and hazy images are exploited in depth-guided hybrid attention Transformer blocks, which adaptively leverage both the cross-attention and self-attention to effectively model hazy densities via cross-modality fusion and capture global context information for better feature representations. In addition, the depth estimations of hazy images are further explored for the detection tasks on hazy images. Extensive experiments demonstrate that the depth estimation not only enhances the model generalization ability across different dehazing datasets, leading to state-of-the-art self-supervised dehazing performance, but also benefits downstream detection tasks on hazy images. Our code is available at https://github.com/DongLiangSXU/Depth-Guidance-dehazing.git.

Multiscale implicit frequency selective network for single‐image dehazing

AbstractImage dehazing is aimed to reconstruct a clear latent image from a degraded image affected by haze. Although vision transformers have achieved impressive success in various computer vision tasks, the limitations in scale and quality of available datasets have hindered the transformer effectiveness for image dehazing. Thus, convolutional neural networks (CNNs) remain the mainstream approach for image dehazing, offering robust performance and adaptability. We further explore the potential of CNNs in image dehazing by proposing a multiscale implicit frequency selection network (MIFSN). The proposed MIFSN enhances multiscale representation learning based on U‐shaped networks. As hazy and clear images considerably differ in high‐frequency components, we introduce an implicit frequency selection module to amplify high‐frequency components of features and generate candidate feature maps. Implicit frequency selection attention is then used to emphasize and merge beneficial frequency components. Results from extensive experiments on synthetic and real‐world datasets demonstrate the superior performance of MIFSN for image dehazing.

Adaptive haze pixel intensity perception transformer structure for image dehazing networks.

In the realm of deep learning-based networks for dehazing using paired clean-hazy image datasets to address complex real-world haze scenarios in daytime environments and cross-dataset challenges remains a significant concern due to algorithmic inefficiencies and color distortion. To tackle these issues, we propose SwinTieredHazymers (STH), a dehazing network designed to adaptively discern pixel intensities in hazy images and compute haze residue for clarity restoration. Through a unique three-branch design, we hierarchically modulate haze residuals by leveraging the global features brought by Transformer and the local features brought by Convolutional Neural Network (CNN) which has led to the algorithm's widespread applicability. Experimental results demonstrate that our approach surpasses advanced single-image dehazing methods in both quantitative metrics and visual fidelity for real-world hazy image dehazing, while also exhibiting strong performance in cross-dataset dehazing scenarios.

SCL-Dehaze: Toward Real-World Image Dehazing via Semi-Supervised Codebook Learning

Existing dehazing methods deal with real-world haze images with difficulty, especially scenes with thick haze. One of the main reasons is lacking real-world pair data and robust priors. To improve dehazing ability in real-world scenes, we propose a semi-supervised codebook learning dehazing method. The codebook is used as a strong prior to guide the hazy image recovery process. However, the following two issues arise when the codebook is applied to the image dehazing task: (1) Latent space features obtained from the coding of degraded hazy images suffer from matching errors when nearest-neighbour matching is performed. (2) Maintaining a good balance of image recovery quality and fidelity for heavily degraded dense hazy images is difficult. To reduce the nearest-neighbor matching error rate in the vector quantization stage of VQGAN, we designed the unit dual-attention residual transformer module (UDART) to correct the latent space features. The UDART can make the latent features obtained from the encoding stage closer to those of the corresponding clear image. To balance the quality and fidelity of the dehazing result, we design a haze density guided weight adaptive module (HDGWA), which can adaptively adjust the multi-scale skip connection weights according to haze density. In addition, we use mean teacher, a semi-supervised learning strategy, to bridge the domain gap between synthetic and real-world data and enhance the model generalization in real-world scenes. Comparative experiments show that our method achieves improvements of 0.003, 2.646, and 0.019 over the second-best method for the no-reference metrics FADE, MUSIQ, and DBCNN, respectively, on the real-world dataset URHI.

Adaptive Multi-Feature Attention Network for Image Dehazing

Currently, deep-learning-based image dehazing methods occupy a dominant position in image dehazing applications. Although many complicated dehazing models have achieved competitive dehazing performance, effective methods for extracting useful features are still under-researched. Thus, an adaptive multi-feature attention network (AMFAN) consisting of the point-weighted attention (PWA) mechanism and the multi-layer feature fusion (AMLFF) is presented in this paper. We start by enhancing pixel-level attention for each feature map. Specifically, we design a PWA block, which aggregates global and local information of the feature map. We also employ PWA to make the model adaptively focus on significant channels/regions. Then, we design a feature fusion block (FFB), which can accomplish feature-level fusion by exploiting a PWA block. The FFB and PWA constitute our AMLFF. We design an AMLFF, which can integrate three different levels of feature maps to effectively balance the weights of the inputs to the encoder and decoder. We also utilize the contrastive loss function to train the dehazing network so that the recovered image is far from the negative sample and close to the positive sample. Experimental results on both synthetic and real-world images demonstrate that this dehazing approach surpasses numerous other advanced techniques, both visually and quantitatively, showcasing its superiority in image dehazing.

A novel multiscale cGAN approach for enhanced salient object detection in single haze images

In computer vision, image dehazing is a low-level task that employs algorithms to analyze and remove haze from images, resulting in haze-free visuals. The aim of Salient Object Detection (SOD) is to locate the most visually prominent areas in images. However, most SOD techniques applied to visible images struggle in complex scenarios characterized by similarities between the foreground and background, cluttered backgrounds, adverse weather conditions, and low lighting. Identifying objects in hazy images is challenging due to the degradation of visibility caused by atmospheric conditions, leading to diminished visibility and reduced contrast. This paper introduces an innovative approach called Dehaze-SOD, a unique integrated model that addresses two vital tasks: dehazing and salient object detection. The key novelty of Dehaze-SOD lies in its dual functionality, seamlessly integrating dehazing and salient object identification into a unified framework. This is achieved using a conditional Generative Adversarial Network (cGAN) comprising two distinct subnetworks: one for image dehazing and another for salient object detection. The first module, designed with residual blocks, Dark Channel Prior (DCP), total variation, and the multiscale Retinex algorithm, processes the input hazy images. The second module employs an enhanced EfficientNet architecture with added attention mechanisms and pixel-wise refinement to further improve the dehazing process. The outputs from these subnetworks are combined to produce dehazed images, which are then fed into our proposed encoder–decoder framework for salient object detection. The cGAN is trained with two modules working together: the generator aims to produce haze-free images, whereas the discriminator distinguishes between the generated haze-free images and real haze-free images. Dehaze-SOD demonstrates superior performance compared to state-of-the-art dehazing methods in terms of color fidelity, visibility enhancement, and haze removal. The proposed method effectively produces high-quality, haze-free images from various hazy inputs and accurately detects salient objects within them. This makes Dehaze-SOD a promising tool for improving salient object detection in challenging hazy conditions. The effectiveness of our approach has been validated using benchmark evaluation metrics such as mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM).

A comprehensive qualitative and quantitative survey on image dehazing based on deep neural networks

Image dehazing has become a necessary area of research with the increasing popularity and demand of computer vision systems. Image dehazing is a method to remove haze from an image to improve its visual quality. Dehazing techniques are widely employed in a variety of computer vision applications to enhance their overall performance. Many techniques have been proposed by researchers in recent years to eliminate the haze from an image. However, there is a lack of available literature that provides a summary of deep learning-based state-of-the-art image dehazing methods. In this study, we provide a detailed review of recently proposed image dehazing techniques based on deep neural networks such as CNN, GAN, RNN, RCNN, and Transformer. A concise review of significant applications of image dehazing, benchmark datasets, and various performance metrics are also presented. We compare the state-of-the-art methods quantitatively using performance evaluation metrics such as SSIM and PSNR. Finally, this study discusses the fundamental difficulties associated with image dehazing approaches that need to be further explored.

PSD-ELGAN: A pseudo self-distillation based CycleGAN with enhanced local adversarial interaction for single image dehazing

Compared to pixel-level content loss, domain-level style loss in CycleGAN-based dehazing algorithms just imposes relatively soft constraints on the intermediate translated images, resulting in struggling to accurately model haze-free features from real hazy scenes. Furthermore, globally perceptual discriminator may misclassify real hazy images with significant scene depth variations as clean style, thereby resulting in severe haze residue. To address these issues, we propose a pseudo self-distillation based CycleGAN with enhanced local adversarial interaction for image dehazing, termed as PSD-ELGAN. On the one hand, we leverage the characteristic of CycleGAN to generate pseudo image pairs during training. Knowledge distillation is employed in this unsupervised framework to transfer the informative high-quality features from the self-reconstruction network of real clean images to the dehazing generator of paired pseudo hazy images, which effectively improves its haze-free feature representation ability without increasing network parameters. On the other hand, in the output of dehazing generator, four non-uniform image patches severely affected by residual haze are adaptively selected as input samples. The local discriminator could easily distinguish their hazy style, thereby further compelling the dehazing generator to suppress haze residues in such regions, thus enhancing its dehazing performance. Extensive experiments show that our PSD-ELGAN can achieve promising results and better generality across various datasets.

Fast no-reference deep image dehazing

This paper presents a deep learning method for image dehazing and clarification. The main advantages of the method are high computational speed and using unpaired image data for training. The method adapts the Zero-DCE approach (Li et al. in IEEE Trans Pattern Anal Mach Intell 44(8):4225–4238, 2021) for the image dehazing problem and uses high-order curves to adjust the dynamic range of images and achieve dehazing. Training the proposed dehazing neural network does not require paired hazy and clear datasets but instead utilizes a set of loss functions, assessing the quality of dehazed images to drive the training process. Experiments on a large number of real-world hazy images demonstrate that our proposed network effectively removes haze while preserving details and enhancing brightness. Furthermore, on an affordable GPU-equipped laptop, the processing speed can reach 1000 FPS for images with 2K resolution, making it highly suitable for real-time dehazing applications.

A novel image dehazing algorithm for complex natural environments

As an essential preprocessing step in computer vision tasks, image dehazing is preparatory work in many practical applications. To ensure the navigation safety of intelligent ships in complex haze weather, this paper proposes a lightweight real-time dehazing method for the natural environment based on an all-in-one dehazing network. Specifically, a hybrid dilated convolution is used to construct a dehazing network, which effectively expands the receptive field and improves the ability to extract characteristic information without increasing the calculation amount of the model. Secondly, a hybrid attention module is developed to enhance the model's ability to remove haze while retaining more detailed features by giving weights to light haze and heavy haze areas in the image. Finally, considering the subjective perception of human eyes, a mixed loss function is designed to address image dimness and color distortion after dehazing. Through experiments on both synthetic and real-world haze datasets, the proposed method attains ideal dehazing results and has high real-time performance. It can dehaze under different haze conditions, address the critical challenge of degraded visual perception in severe weather conditions of intelligent ship navigation on inland rivers in complex environments, and promote intelligent ships' development.

TSMD-Net: A two-stage mixed dehazing network with feature fusion and multi-window self attention on Jetson board

Single image dehazing is a pivotal endeavor focused on enhancing the quality of images by removing haze-induced noise. Achieving this goal requires a delicate equilibrium between retaining spatial intricacies and contextual understanding, particularly crucial in real-world scenarios where complexity is amplified. In response to this challenge, we present the Two-Stage Mixed Dehazing Network (TSMD-Net), designed to reconcile these conflicting objectives by leveraging the foundational U-Net architecture. Our approach entails a dual-phase strategy: initially assimilating contextual comprehension through encoder-decoder frameworks, followed by amalgamating it with high-resolution branches to uphold spatial integrity. To streamline the analysis and comparison of this architecture, we opt to substitute or eliminate nonlinear activation functions with multiplication operations, effectively mitigating system complexity. Furthermore, to foster seamless information exchange across hierarchical layers of the encoder-decoder at varying scales, we introduce the Feature Fusion (FF) mechanism, facilitating the integration of insights from higher layers progressively down to the base layer. This amalgamated data significantly enhances the fidelity of the original image transformation by harmonizing the inclusion of contextual feature maps with the preservation of spatial details, resulting in noise-free, high-quality images. Additionally, we propose the “Multi-Window Self Attention” (MWSA) mechanism, characterized by linear time complexity, as the central block of the encoder-decoder. This module mitigates the confined receptive fields inherent in convolutional neural networks (CNNs), enabling the aggregation of more comprehensive feature-map data. Subsequently, our TSMD-Net undergoes rigorous training and testing on both synthetic and real-world datasets, showcasing superior performance compared to current state-of-the-art (SOTA) networks while demanding fewer processing resources. To validate the robustness and practical utility of our methodology, we implement TSMD-Net on a low-end Jetson Nano board, confirming its effectiveness and efficiency in real-world settings.

Compensation Atmospheric Scattering Model and Two-Branch Network for Single Image Dehazing

Compensation Atmospheric Scattering Model and Two-Branch Network for Single Image Dehazing