Search

Images captured in foggy or hazy environments exhibit whitening and reduced contrast. Various methods have been proposed to enhance the visibility of hazy images with degraded quality. However, these conventional methods may cause degradation of the resultant image, such as unnatural color changes and artifacts. To address this problem, we propose a haze-removal method for image visibility improvement using manipulation of convex combination coefficients of equi-hue planes’ vertices, which correspond to white, black, and pure colors in the RGB color space. The proposed method begins with a pre-processing step involving contrast enhancement of the lightness component using a multi-scale image enhancement method with S-shaped functions. Then, for saturation enhancement, the convex combination coefficients of white are reduced, and those of pure colors are increased in the lightness-enhanced image to achieve haze removal. Experiments were conducted using real hazy images and artificially synthesized images, and the effectiveness of the proposed method was verified by comparison with conventional methods. The experimental results demonstrate that the proposed method effectively enhances the visibility of hazy images while minimizing unnatural color changes and artifacts. [ABSTRACT FROM AUTHOR]

Haze obscures remote sensing images, making it difficult to extract valuable information. To address this problem, we propose a fine detail extraction network that aims to restore image details and improve image quality. Specifically, to capture fine details, we design multi-scale and multi-dimensional extraction blocks and then fuse them to optimize feature extraction. The multi-scale extraction block adopts multi-scale pixel attention and channel attention to extract and combine global and local information from the image. Meanwhile, the multi-dimensional extraction block uses depthwise separable convolutional layers to capture additional dimensional information. Additionally, we integrate an atmospheric scattering model unit into the network to enhance both the dehazing effectiveness and stability. Our experiments on the SateHaze1k and HRSD datasets demonstrate that the proposed method efficiently handles remote sensing images with varying levels of haze, successfully recovers fine details, and achieves superior results compared to existing state-of-the-art dehazing techniques. [ABSTRACT FROM AUTHOR]

Haze is a natural phenomenon that negatively affects image clarity and quality, posing challenges across various image-related applications. Traditional dehazing models often suffer from overfitting when trained on synthetic hazy-clean image pairs, which do not generalize well to real-world hazy conditions. To tackle this, recent methodologies have explored training models on unpaired data, better reflecting the variability encountered in natural scenes. This dual capability of CycleGAN is particularly beneficial for overcoming the overfitting issues associated with synthetic datasets. By incorporating CycleGAN into our DehazeDNet framework, we ensure that our dehazing model not only translates images effectively but also respects the physical characteristics of haze. Inspired by the D4 model, our approach includes a Depth Evaluation Block to estimate scene depth from images. Since haze density often correlates with scene depth, this depth information is crucial for accurate haze modeling. We utilize the U-Net architecture for the Depth Evaluation Block due to its proven efficiency in image-to-image translation tasks. To preserve the accuracy of the dehazed images, we incorporate an identity loss function into our model. Identity loss ensures that the dehazed output retains the essential characteristics of the input image. Our results demonstrate an increase in SSIM and PSNR compared to other unsupervised dehazing models, highlighting the efficiency of our method in maintaining image quality and details while removing haze. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative single image dehazing framework, termed Regional Saturation-Value Translation (RSVT), to address the color distortion problems commonly encountered in bright regions by conventional dehazing approaches. The proposed RSVT framework is developed based on two key insights derived from the HSV color space: first, the hue component shows negligible variation between corresponding hazy and haze-free points; and second, in the 2D saturation-value coordinate system, the majority of lines connecting hazy-clean point pairs tend to converge near the atmospheric light coordinates. Consequently, haze removal can be achieved through appropriate translations within the saturation-value coordinates. Additionally, a robust soft segmentation method that employs a morphological min-max channel is integrated into the framework. By combining the soft segmentation mask with the RSVT prior, a comprehensive single image dehazing framework is established. Experimental evaluations across various datasets demonstrate that the proposed approach effectively mitigates color distortion and successfully restores visually appealing images. Moreover, a case study involving actual flight test demonstrates the feasibility and effectiveness of the proposed approach in real-world scenarios. The code is available at https://github.com/tranleanh/rsvt.

Summary: The experienced doctors can alleviate symptoms such as headaches, insomnia, anxiety, and depression by observing the patient's ears and massaging specific areas. In order to achieve remote ear condition diagnosis and guide patients to massage their ears independently through the network, patients can use their mobile phones to take and send photos of ears to doctors. However, due to significant differences in the clarity of photos taken by different mobile phones, as well as susceptibility to haze, lighting, jitter, and low pixels, the quality of photos is poor, which affects the accuracy of remote diagnosis by doctors. This study adopted an image preprocessing method based on He Kaiming's dark channel prior dehazing method to enhance the original ear images captured by mobile phones. The dehazing algorithm was used to remove the haze effect of the ear images, improving image quality and contrast, making the wrinkles, protrusions, pigmentation and other areas of the ear more obvious. The experiment has showed the comparison by adjusting weight from 15% to 95% between two methods—dark channel prior method and the dark channel prior method after preprocessing, which has proven the effectiveness of dehazing method in human ear images taken by mobile phones. The image quality after preprocessing and dehazing is widely recognized and accepted by doctors at hospitals in Hangzhou, China. [ABSTRACT FROM AUTHOR]

Haze imagery suffers from reduced clarity, which can be attributed to atmospheric conditions such as dust or water vapor, resulting in blurred visuals and heightened brightness due to light scattering. Conventional methods employing the dark channel prior (DCP) for transmission map estimation often excessively amplify fogged sky regions, causing image distortion. This paper presents a novel approach to improve transmission map granularity by utilizing multiple 1 × 1 DCPs derived from multiscale hazy, inverted, and Euclidean difference images. An adaptive airlight estimation technique is proposed to handle low-light, hazy images. Furthermore, an adaptive gamma correction method is introduced to refine the transmission map further. Evaluation of dehazed images using the Dehazing Quality Index showcases superior performance compared to existing techniques, highlighting the efficacy of the enhanced transmission map. [ABSTRACT FROM AUTHOR]

The polarization information of incident light gets lost after passing through the scattering medium such as haze. This phenomenon, called depolarization, poses a challenge to polarization remote sensing of ships in sea-haze weather. In this paper, we propose the concept of ship polarization information restoration for the first time and propose a ship polarization information restoration method for the benefit of ship remote sensing in sea haze weather. The approach is based on the estimation of airlight, a component of hazy polarimetric images. To demonstrate the effectiveness of the proposed ship polarization information restoration method, we conduct an outdoor experiment on ship polarization information restoration in sea haze weather. Experimental results show that sea haze significantly degrades the degree of polarization of ships, but slightly affects the angle of polarization. In addition, the degree of polarization of ships becomes more prominent after polarization information restoration. [ABSTRACT FROM AUTHOR]

Hazy weather reduces contrast, narrows the dynamic range, and blurs the details of the remote sensing image. Additionally, color fidelity deteriorates, causing color shifts and image distortion, thereby impairing the utility of remote sensing data. In this paper, we propose a lightweight remote sensing-image-dehazing network, named LRSDN. The network comprises two tailored, lightweight modules arranged in cascade. The first module, the axial depthwise convolution and residual learning block (ADRB), is for feature extraction, efficiently expanding the convolutional receptive field with little computational overhead. The second is a feature-calibration module based on the hybrid attention block (HAB), which integrates a simplified, yet effective channel attention module and a pixel attention module embedded with an observational prior. This joint attention mechanism effectively enhances the representation of haze features. Furthermore, we introduce a novel method for remote sensing hazy image synthesis using Perlin noise, facilitating the creation of a large-scale, fine-grained remote sensing haze image dataset (RSHD). Finally, we conduct both quantitative and qualitative comparison experiments on multiple publicly available datasets. The results demonstrate that the LRSDN algorithm achieves superior dehazing performance with fewer than 0.1M parameters. We also validate the positive effects of the LRSDN in road extraction and land cover classification applications. [ABSTRACT FROM AUTHOR]

Abstract Remote sensing is gradually playing an important role in the detection of ground information. However, the quality of remote-sensing images has always suffered from unexpected natural conditions, such as intense haze phenomenon. Recently, convolutional neural networks (CNNs) have been applied to deal with dehazing problems, and some important findings have been obtained. Unfortunately, the performance of these classical CNN-based methods still needs further enhancement owing to their limited feature extraction capability. As a critical branch of CNNs, the generative adversarial network (GAN), composed of a generator and discriminator, has become a hot research topic and is considered a feasible approach to solving the dehazing problems. In this study, a novel dehazed generative adversarial network (GAN) is proposed to reconstruct the clean images from the hazy ones. For the generator network of the proposed GAN, the color and luminance feature extraction module and the high-frequency feature extraction module aim to extract multi-scale features and color space characteristics, which help the network to acquire texture, color, and luminance information. Meanwhile, a color loss function based on hue saturation value (HSV) is also proposed to enhance the performance in color recovery. For the discriminator network, a parallel structure is designed to enhance the extraction of texture and background information. Synthetic and real hazy images are used to check the performance of the proposed method. The experimental results demonstrate that the performance can significantly improve the image quality with a significant increment in peak-signal-to-noise ratio (PSNR). Compared with other popular methods, the dehazing results of the proposed method closely resemble haze-free images.

When gathering optical satellite pictures, light reflected from the surface due to water vapor, snow, fog, haze, and more tiny particles in the environment is generally seen as a gap in the propagation process. Haze has a greater number of suspended particles like aerosols and water droplets. These particles have absorption effects and scattering in the light. Although haze translucency grants a chance for image restoration, a well-organized and broadly relevant haze removal procedure for holding several hazes is quite an extensive provocation. To address this issue, this paper proposed an Optimal Contrast Limited Adaptive Histogram Equalization (OCLAHE) to capture further intricate features and patterns connected to haze, enabling more accurate haze detection and removal. The deeper network can learn complicated descriptions and recognize between hazy and nonhazy regions with higher precision. The proposed method is validated in I-Haze and O-Haze datasets, and its performance is quantified by various performance metrics such as MSE, SSIM, PSNR, WPSNR, and Running time. The experimental consequences demonstrate that the developed model performs better than other techniques and attains an MSE from both datasets as 0.0054 and 0.0051. Overall, the proposed model amends the accuracy and reliability of haze detection in images. [ABSTRACT FROM AUTHOR]

Haze obscures remote sensing images, making it difficult to extract valuable information. To address this problem, we propose a fine detail extraction network that aims to restore image details and improve image quality. Specifically, to capture fine details, we design multi-scale and multi-dimensional extraction blocks and then fuse them to optimize feature extraction. The multi-scale extraction block adopts multi-scale pixel attention and channel attention to extract and combine global and local information from the image. Meanwhile, the multi-dimensional extraction block uses depthwise separable convolutional layers to capture additional dimensional information. Additionally, we integrate an atmospheric scattering model unit into the network to enhance both the dehazing effectiveness and stability. Our experiments on the SateHaze1k and HRSD datasets demonstrate that the proposed method efficiently handles remote sensing images with varying levels of haze, successfully recovers fine details, and achieves superior results compared to existing state-of-the-art dehazing techniques.

Managing unforeseen situations, particularly in low-visibility environments caused by weather degradation, continues to be a significant challenge for the autonomous driving vision system. This paper aims to enhance the visibility of degraded images captured by the system's sensors by removing haze. To achieve this goal, we propose an algorithm that predicts transmission from a regression model using random forest and atmospheric light using a quad-tree decomposition method. We evaluate the performance of the haze removal algorithm on three benchmark datasets (FRIDA2, D-HAZY, and RESIDE) using both quantitative and qualitative analyses. Our proposed method yields the lowest count of saturated pixels (∑) in blind contrast enhancement assessment, with ∑ = 0.0001. The implications of our approach are significant. By utilizing the RF-transmission estimation and quad-based atmospheric light prediction, the proposed haze removal algorithm demonstrates greater robustness in preventing unintended black or white color pixels in the dehazed image. This improvement can contribute to safer autonomous driving, particularly in low-visibility conditions, where the reliability of image processing systems is paramount. [ABSTRACT FROM AUTHOR]

This paper proposes an image dehazing prior, called Regional Saturation-Value Translation (RSVT), in order to address the color distortion issues produced by prevailing prior-based dehazing methods when processing hazy images with large sky regions. The proposed RSVT prior is derived from statistical analyses of the correlation between hazy points and respective haze-free points in the HSV color space. The prior is based upon two key observations in the sky areas. First, the difference in terms of hue for a pair of hazy and haze-free points is very small, raising an assumption that the variability of pixel values caused by haze mostly occurs in the saturation and value spaces. This leads to the second observation that, in the 2D saturation-value coordinate system, almost all the lines passing through corresponding pairs of hazy-clean points, termed S-V lines, are likely to intersect around the airlight coordinates. A hybrid refined dark channel is introduced in order to decompose the input hazy image into sky and non-sky regions and to estimate the global atmospheric light. Combining the prior with the hybrid refined dark channel, a novel single image dehazing framework is proposed. Haze removal is performed separately for the sky and non-sky regions by adopting the proposed RSVT prior and Koschmieder's law, respectively. The experimental results have shown that the proposed dehazing method can restore visually compelling sky color and effectively handle the color distortion issues associated with large sky regions. [ABSTRACT FROM AUTHOR]

Infrared image has lower resolution, lower contrast, and less detail than visible image, which causes its super-resolution (SR) more difficult than visible image. This paper presents an approach based on a deep neural network that comprises an image SR branch and a gradient SR branch to reconstruct high-quality SR image from single-frame infrared image. The image SR branch reconstructs the SR image from the initial low-resolution infrared image using a basic structure similar to the enhanced SR generative adversarial network (ESRGAN). The gradient SR branch removes haze, extracts the gradient map, and reconstructs the SR gradient map. To obtain more natural SR image, a fusion block based on attention mechanism is adopted between these branches. To preserve the geometric structure, gradient L1 loss and gradient GAN loss are defined and added. Experimental results on a public infrared image dataset demonstrate that, compared with the current SR methods, the proposed method is more natural and realistic, and can better preserve the structures. [ABSTRACT FROM AUTHOR]

We proposed a novel image-enhancing framework to ensure consolidated restoration accuracy when remedying the visual quality of dehazed images, such as over-saturation, color deviation, or luminance issues. Conventionally, the dehazing process was usually considered image restoration; however, enhancement methods only aimed to improve dehazed images’ contrast, brightness, and detail. Therefore, the conventional enhancing framework tended to degrade consolidated restoration accuracy due to focusing on low-level image features rather than controlling errors associated with the dehazing process. Our method aimed to improve the consolidated restoration accuracy of the dehazing-and-enhancing process. In experiments, the proposed framework improved the visual quality and preserved restoration accuracy (despite enhancements) with high computational efficiency and resolved quality issues generated by conventional dehazing algorithms. Moreover, the minimal time complexity of the proposed framework is O(n), ensuring practical applicability when implemented in conjunction with state-of-the-art dehazing algorithms.

Due to the influence of rainy and foggy weather, obtaining clear images becomes more challenging, often resulting in low visibility, poor contrast, and missing detail information. To address these issues, a robust image defogging algorithm is proposed. Firstly, the input image undergoes conversion into a detailed image, with attenuation and redefinition of its three color channels. Color compensation and balance are then applied based on the principle of minimizing color loss. Secondly, the problem of image darkening is tackled through an improved atmospheric scattering model (EASM) and the dark channel a priori algorithm. The defogging results exhibit noticeable enhancements in terms of bright colors and clear details. In the natural images showcased in the paper, the proposed algorithm achieves improvements in information entropy, the fog density evaluator (FADE), and the natural image quality evaluator (NIQE) by 0.46%, 9.7%, and 12.0%, respectively, compared to the suboptimal algorithm. In the synthetic image datasets I-HAZE and O-HAZE, there are enhancements in information entropy by 0.19% and 0.76%, respectively, and in NIQE by 1.05% each, albeit slightly lower than the sub-optimal results. The structural similarity (SSIM) also sees improvements of 6.3% and 10.9% compared to the suboptimal results in FADE. These findings demonstrate the superior performance of the proposed algorithm over the latest defogging algorithms in terms of information entropy, FADE, NIQE, and SSIM, underscoring its high robustness and promising application prospects.

When imaging the Earth's surface, space-based optical imaging sensors are inevitably interfered by scattering media, such as clouds and haze, resulting in serious degradation of remote sensing images they capture. To enhance the quality of remote sensing images and mitigate the influence of clouds, haze, and other media, we construct a novel approach called the inverse cognitive learning network. The network mainly consists of multiscale inverse cognitive learning blocks that we designed. It has the capability to extract image features at multiple scales, adaptively focus on the global information and location-related local information, and effectively constrain the haze. In the multiscale inverse cognitive learning block, we embed the designed inverse cognitive learning module and parallel haze constraint module. The inverse cognitive learning module simulates the inverse process of human brain cognitive image, and gradually learns the haze information from the depth, moderate, and breadth channel features. The parallel haze constraint module integrates the extracted haze information through a dual-branch approach to realize strong constraints on haze features. Experimental results indicate that our approach notably enhances the clarity of remote sensing images that suffer from cloud cover and haze, and possesses more perfect haze removal effect and robustness than state-of-the-art dehazing approaches.

Haze imagery suffers from reduced clarity, which can be attributed to atmospheric conditions such as dust or water vapor, resulting in blurred visuals and heightened brightness due to light scattering. Conventional methods employing the dark channel prior (DCP) for transmission map estimation often excessively amplify fogged sky regions, causing image distortion. This paper presents a novel approach to improve transmission map granularity by utilizing multiple 1×1 DCPs derived from multiscale hazy, inverted, and Euclidean difference images. An adaptive airlight estimation technique is proposed to handle low-light, hazy images. Furthermore, an adaptive gamma correction method is introduced to refine the transmission map further. Evaluation of dehazed images using the Dehazing Quality Index showcases superior performance compared to existing techniques, highlighting the efficacy of the enhanced transmission map.

Autonomous vehicle navigation technology is increasing rapidly. However, automatic sign recognition in complex illumination environments like low-light, hazy regions is a significant challenge in in-vehicle navigation. So, haze removal and robust traffic sign detection and recognition (TSDR) are critical for ensuring the vehicle's and its passengers' safety. However, the conventional methods failed to perform both haze removal and TSDR operations simultaneously. Further, the conventional haze removal methods eliminate the wanted pixels, still the presence of haze, which results in reduced traffic sign detection performance. Moreover, the conventional sign recognition methods classify a few types of traffic signs. So, this work aims to develop a unified model for multi-class sign recognition in complex environmental conditions. Therefore, this work introduced the deep learning model for haze removal based on TSDR (DLHR-TSDR). Initially, the CURE-TSD dataset is considered. The haze removal U-network (HRU-Net) module inputs a hazy image and outputs a haze-free image trained to learn the mapping between hazy and haze-free images. Then, the TSDR-convolutional neural network (CNN) module takes the haze-free image from the previous module as input and outputs the location traffic signs in the image. The simulation results on the Carleton University Retinal Eye-Traffic Sign Dataset (CURE-TSD) dataset show that the DLHR-TSDR method developed in the study resulted in 99.01 % accuracy, higher than traditional methods.

Space probes are always obstructed by floating objects in the atmosphere (clouds, haze, rain, etc.) during imaging, resulting in the loss of a significant amount of detailed information in remote sensing images and severely reducing the quality of the remote sensing images. To address the problem of detailed information loss in remote sensing images, we propose an end-to-end detail enhancement network to directly remove haze in remote sensing images, restore detailed information of the image, and improve the quality of the image. In order to enhance the detailed information of the image, we designed a multi-scale detail enhancement unit and a stepped attention detail enhancement unit, respectively. The former extracts multi-scale information from images, integrates global and local information, and constrains the haze to enhance the image details. The latter uses the attention mechanism to adaptively process the uneven haze distribution in remote sensing images from three dimensions: deep, middle and shallow. It focuses on effective information such as haze and high frequency to further enhance the detailed information of the image. In addition, we embed the designed parallel normalization module in the network to further improve the dehazing performance and robustness of the network. Experimental results on the SateHaze1k and HRSD datasets demonstrate that our method effectively handles remote sensing images obscured by various levels of haze, restores the detailed information of the images, and outperforms the current state-of-the-art haze removal methods. [ABSTRACT FROM AUTHOR]

Traffic sign recognition is of great significance to promote traffic sustainability and maintain traffic safety. GPS monitoring systems and advanced autonomous vehicles are often heavily reliant on camera imagery. Algorithms based on dark channel prior are susceptible to color distortion when processing traffic images containing bright sky or high-brightness areas, which can negatively impact the identification of traffic signals and signage located in elevated positions. To address this issue, this paper proposes a dehazing algorithm (SRSTO) that combines sky region segmentation and transmittance optimization. Firstly, the gradient, brightness and saturation information are calculated, followed by the construction of a threshold function used in area segmentation. This approach is utilized to partition the image into areas not containing sky highlights and the area that contains them. Subsequently, the dark channel images of the sky and the non-sky regions are acquired, morphological operations are further performed in layers and blocks, and then the atmospheric scattered light value is calculated. Secondly, the functional relationship between the transmittance of the sky region and the brightness of the image is constructed, the transmittance of the sky and the non-sky region are optimized, and the transmittance map is further improved by using guided filtering. A simulated annealing algorithm is employed to intelligently optimize parameters such as sky segmentation threshold and sky brightness area transmittance, followed by improving the adaptability of the algorithm. Finally, combined with Gaussian filtering and Sobel edge enhancement, the image brightness is further adjusted. Using Information Entropy and NIQE as objective evaluation indexes, combined with subjective evaluation, it is concluded that the proposed method has good convergence and self-adaptive ability, and the objective indexes and subjective effects are better, especially for the hazed images containing air traffic signs. [ABSTRACT FROM AUTHOR]

Traditional convolutional neural networks work well on single-image defogging synthetic datasets, but for real-world images with different concentrations, it leads to incomplete defogging or distortion and loss of image detail information. In this paper, the authors propose an end-to-end single-image defogging network, which adopts an encoder–decoder structure, obtains a large perceptual field of view of high pixels through a large number of pooling operations in the U-Net network, and uses operations such as hopping connections to retain most of the image feature information. To achieve superior quality haze removal, the proposed method utilizes a bilateral grid to capture high-frequency information pertaining to the image edges in low-resolution pixels. Additionally, relevant haze-related features are extracted, and a local affine model is fitted within the bilateral space. Finally, the high and low pixel data are integrated with the extracted features to generate clear and vivid images. The authors compare the algorithm qualitatively and quantitatively with several state-of-the-art algorithms and show that the algorithm achieves better defogging results in both the SOTS dataset and real-world images, retains high-frequency image details, achieves higher peak signal-to-noise ratio, and performs better defogging in haze images with different concentrations. [ABSTRACT FROM AUTHOR]

Hazy weather reduces contrast, narrows the dynamic range, and blurs the details of the remote sensing image. Additionally, color fidelity deteriorates, causing color shifts and image distortion, thereby impairing the utility of remote sensing data. In this paper, we propose a lightweight remote sensing-image-dehazing network, named LRSDN. The network comprises two tailored, lightweight modules arranged in cascade. The first module, the axial depthwise convolution and residual learning block (ADRB), is for feature extraction, efficiently expanding the convolutional receptive field with little computational overhead. The second is a feature-calibration module based on the hybrid attention block (HAB), which integrates a simplified, yet effective channel attention module and a pixel attention module embedded with an observational prior. This joint attention mechanism effectively enhances the representation of haze features. Furthermore, we introduce a novel method for remote sensing hazy image synthesis using Perlin noise, facilitating the creation of a large-scale, fine-grained remote sensing haze image dataset (RSHD). Finally, we conduct both quantitative and qualitative comparison experiments on multiple publicly available datasets. The results demonstrate that the LRSDN algorithm achieves superior dehazing performance with fewer than 0.1M parameters. We also validate the positive effects of the LRSDN in road extraction and land cover classification applications.

This paper proposes a new effective scale-aware edge-smoothing weighting constraint-based weighted guided image filter (ESAESWC-WGIF) for single image dehazing. Edge-weighting constraint incorporated in this method is multi-scale and less sensitive to regularization parameter. It removes halo artifacts and over-smoothing strongly and preserves edge information in both flat and sharp regions more accurately than the guided image filter (GIF) and weighted guided image filter (WGIF). There are three main steps in the proposed method: In the first step, dark channel prior method is applied to hazy input image to estimate atmospheric map and transmission map. In the next step, we refine the initial transmission map using the proposed ESAESWC-WGIF. It removes halo artifacts, over-smoothing effect strongly and preserves edge information in both flat and sharp regions. In the final step, the haze-free image is recovered from the scene radiance. About 3200 images from Fattal, NYU2, D-HAZY, Haze-RD, and O-Haze datasets are used to compare the performance of the proposed filter with the existing image dehazing methods. Experimental results prove that the proposed method is independent of the nature of the input image. Moreover, it produces better visual quality. It is noteworthy that the proposed method is faster than the existing methods for a given resolution of images. [ABSTRACT FROM AUTHOR]

In the context of safety and security, the ability to track and identify faces in hazy conditions presents a significant challenge. The deleterious effects of haze on video quality, such as the diminution of detail, reduction in contrast, distortion of color, and complications in depth estimation, impede effective facial recognition. Additionally, the complexity of live video tracking is exacerbated by factors such as occlusion, positional variations, and lighting changes. Despite these challenges, video sequences offer an abundance of information, surpassing static images in terms of potential data extraction. In this study, a dual approach strategy is employed to detect and track faces in hazy conditions. The Kanade-Lucas-Tomasi (KLT) algorithm, celebrated for its adept feature tracking capabilities, is deployed to execute face tracking. The effectiveness of this algorithm lies in its ability to accurately trace points across successive image frames, a crucial aspect of reliable face tracking. Concurrently, the Viola-Jones algorithm is utilized for face detection. The algorithm harnesses Haar-like features to efficiently discern faces in real-time, effectively overcoming the challenge of identifying faces within video frames. To further enhance the quality of the video, the dark channel prior (DCP) image dehazing technique is employed. This technique improves visibility by increasing contrast and color saturation, whilst concurrently identifying and eliminating air haze from the video frames. [ABSTRACT FROM AUTHOR]

We proposed a novel image enhancement framework to raise the visibility of the image’s content. Our primary concern is eliminating haze-like effects and simultaneously increasing images’ brightness. Dehazing and luminance enhancement algorithms are considered standard techniques to overcome these issues. However, natural environments usually involve several unfavorable conditions simultaneously, such as insufficient illumination, blur caused by the haze, and color cast resulting from the sun or scattering; this makes dehazing algorithms challenging to overcome environmental issues. Besides, dehazing algorithms sometimes result in artifacts. The proposed framework solves these issues simultaneously by implementing a double-side enhancement in contrast and brightness based on a new dehazing algorithm. We compare the new dehazing algorithm with others using full-reference benchmarks to ensure performance stability. Afterward, to show the advantage of using the new dehazing algorithm, we evaluate the compatibility between the proposed framework and all dehazing algorithms using non-reference benchmarks. At last, we pair dehazing and luminance enhancement algorithms and compare the combinations with the proposed framework. Eventually, experimental results prove that the new dehazing algorithm outperforms others and is better compatible with the proposed framework. Meanwhile, the proposed framework is superior in contrast and brightness enhancements and outperforms the single dehazing algorithm or the combinations.

The existing remote sensing (RS) image dehazing methods based on deep learning have sought help from the convolutional frameworks. Nevertheless, the inherent limitations of convolution, i.e., local receptive fields and independent input elements, curtail the network from learning the long-range dependencies and nonuniform distributions. To this end, we design an effective RS image dehazing Transformer architecture, denoted as RSDformer. First, given the irregular shapes and nonuniform distributions of haze in RS images, capturing both local and non-local features is crucial for RS image dehazing models. Hence, we propose a detail-compensated transposed attention (DCTA) to extract the global and local dependencies (LDs) across channels. Second, to enhance the ability to learn degraded features and better guide the restoration process, we develop a dual-frequency adaptive block (DFAB) with dynamic filters. Finally, a dynamic gated fusion block (DGFB) is designed to achieve fuse and exchange features across different scales effectively. In this way, the model exhibits robust capabilities to capture dependencies from both global and local areas, resulting in improving image content recovery. Extensive experiments prove that the proposed method obtains more appealing performances against other competitive methods. [ABSTRACT FROM AUTHOR]

Haze contamination is a common issue in very high resolution (VHR) remote sensing images, which inevitably hinders their application. The use of auxiliary images has been a key trend for enhancing haze removal. However, auxiliary VHR images are usually inconsistent with the target hazy VHR images, in terms of different imaging viewpoints, misalignment, or land cover changes, which greatly influence the performance of haze removal. To handle this problem, this paper develops a novel solution that incorporates auxiliary VHR images without any preprocessing or manual selection for haze removal. Accordingly, a gradient-based haze removal network (GHRN) is constructed. The gradient maps of both the target hazy images and corresponding auxiliary images are fed to the GHRN architecture, and hierarchical convolutions are applied to assist feature alignment. Seven state-of-the-art haze removal methods were employed as comparisons in ten simulated and six real scenarios. The experiment results demonstrate the reliability of GHRN. Moreover, GHRN is suitable for auxiliary images with different contaminations (e.g., haze or noise). To the best of our knowledge, this is the first study to consider inconsistent auxiliary images for enhancing haze removal.

In this work, a simple and unique, yet effective framework for visibility enhancement, using image decomposition, adaptive boundary constraint and quadtree-based dehazing, detail enhancement and fusion is proposed. The input image firstly undergoes image decomposition, to be split into its its ambient illumination and reflex lightness components. For increasing brightness and contrast, contrast-based enhancement algorithm is applied to the reflex lightness component and the ambient illumination component is dehazed through the application of atmospheric scattering model. In order to estimate the airlight, instead of using the whole image, a simple and efficient method based on quadtree decomposition is used. The transmission map is computed through contextual regularization using adaptive boundary constraints. The dehazed ambient illumination component is passed through detail enhancement for enhancement of sharp edges. The resultant image and the enhanced reflex lightness component are then combined together through fusion to obtain the final, artifact free, enhanced image with preserved colors and details. The proposed methodology is evaluated using numerous images and compared with 8 different state-of-the-art techniques. Visual and quantitative comparison of the proposed methodology with existing state-of-the-art techniques demonstrates the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

Space probes are always obstructed by floating objects in the atmosphere (clouds, haze, rain, etc.) during imaging, resulting in the loss of a significant amount of detailed information in remote sensing images and severely reducing the quality of the remote sensing images. To address the problem of detailed information loss in remote sensing images, we propose an end-to-end detail enhancement network to directly remove haze in remote sensing images, restore detailed information of the image, and improve the quality of the image. In order to enhance the detailed information of the image, we designed a multi-scale detail enhancement unit and a stepped attention detail enhancement unit, respectively. The former extracts multi-scale information from images, integrates global and local information, and constrains the haze to enhance the image details. The latter uses the attention mechanism to adaptively process the uneven haze distribution in remote sensing images from three dimensions: deep, middle and shallow. It focuses on effective information such as haze and high frequency to further enhance the detailed information of the image. In addition, we embed the designed parallel normalization module in the network to further improve the dehazing performance and robustness of the network. Experimental results on the SateHaze1k and HRSD datasets demonstrate that our method effectively handles remote sensing images obscured by various levels of haze, restores the detailed information of the images, and outperforms the current state-of-the-art haze removal methods.

Images degraded by light scattering and absorption, such as hazy, sandstorm, and underwater images, often suffer color distortion and low contrast because of light traveling through turbid media. In order to enhance and restore such images, we first estimate ambient light using the depth-dependent color change. Then, via calculating the difference between the observed intensity and the ambient light, which we call the scene ambient light differential, scene transmission can be estimated. Additionally, adaptive color correction is incorporated into the image formation model (IFM) for removing color casts while restoring contrast. Experimental results on various degraded images demonstrate the new method outperforms other IFM-based methods subjectively and objectively. Our approach can be interpreted as a generalization of the common dark channel prior (DCP) approach to image restoration, and our method reduces to several DCP variants for different special cases of ambient lighting and turbid medium conditions.

Remote sensing (RS) image dehazing is an effective means to enhance the quality of hazy RS images. However, existing dehazing methods are ineffective in dealing with nonhomogeneous RS haze scenes. To tackle this deficiency, we design a multi-model joint estimation (M2JE) module and a self-correcting (SC) module to construct a unified end-to-end network for RS image dehazing, termed the multi-model SC network (M2SCN). Specifically, the M2JE module regards the dehazing process as a multi-model ensemble problem, so as to improve the generalization ability of the model. The SC module can gradually correct the error in the intermedia features extracted by the network, thus enabling the network to deal with nonhomogeneous hazy images. Extensive experiments are conducted to demonstrate that our proposed M2SCN performs favorably against state-of-the-art methods on popular RS image dehazing benchmark datasets. [ABSTRACT FROM AUTHOR]

GPS monitoring systems and the development of driverless vehicles are almost inseparable from camera images. The images taken by traffic cameras often contain certain sky areas and noise, the traditional dark channel prior (DCP) algorithm easily produces color distortion and halo effect, when processing the hazy traffic images with sky and high brightness areas. An optimized Retinex model and dark channel prior algorithm (ORDCP) is proposed in this paper. Firstly by adjusting the calculation method of dark channel image, the proportion of dark channel is improved; Then, the transmittance image is corrected and smoothed by guided filtering and mean filtering. Finally, the Retinex model is fused to save the details. ORDCP corrects the inaccurate calculation of scene transmittance value in DCP algorithm, and modifies some dehazing problems, such as the loss of details, halo effect, contrast and color distortion, etc. Using information entropy (IE) as the objective evaluation index, combined with the subjective evaluation, it is concluded that the algorithm proposed in this paper can effectively retain the detailed information of the image, and eliminate the halo effect. Meanwhile, it meets the visual characteristics of human eyes better, and has some practicality and applicability in traffic control and intelligent detection. [ABSTRACT FROM AUTHOR]