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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.

In order to avoid the noise diffusion and amplification caused by traditional dehazing algorithms, a single image haze removal algorithm based on nonsubsampled contourlet transform (HRNSCT) is proposed. The HRNSCT removes haze only from the low-frequency components and suppresses noise in the high-frequency components of hazy images, preventing noise amplification caused by traditional dehazing algorithms. First, the nonsubsampled contourlet transform (NSCT) is used to decompose each channel of a hazy and noisy color image into low-frequency sub-band and high-frequency direction sub-bands. Second, according to the low-frequency sub-bands of the three channels, the color attenuation prior and dark channel prior are combined to estimate the transmission map, and use the transmission map to dehaze the low frequency sub-bands. Then, to achieve the noise suppression and details enhancement of the dehazed image, the high-frequency direction sub-bands of the three channels are shrunk, and those shrunk sub-bands are enhanced according to the transmission map. Finally, the nonsubsampled contourlet inverse transform is performed on the dehazed low-frequency sub-bands and enhanced high-frequency sub-bands to reconstruct the dehazed and noise-suppressed image. The experimental results show that the HRNSCT provides excellent haze removal and noise suppression performance and prevents noise amplification during dehazing, making it well suited for removing haze from noisy images.

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The visibility and analyzability of MRI and CT images have a great impact on the diagnosis of medical diseases. Therefore, for low-quality MRI and CT images, it is necessary to effectively improve the contrast while suppressing the noise. In this paper, we propose an enhancement and denoising strategy for low-quality medical images based on the sequence decomposition Retinex model and the inverse haze removal approach. To be specific, we first estimate the smoothed illumination and de-noised reflectance in a successive sequence. Then, we apply a color inversion from 0-255 to the estimated illumination, and introduce a haze removal approach based on the dark channel prior to adjust the inverted illumination. Finally, the enhanced image is generated by combining the adjusted illumination and the de-noised reflectance. As a result, improved visibility is obtained from the processed images and inefficient or excessive enhancement is avoided. To verify the reliability of the proposed method, we perform qualitative and quantitative evaluation on five MRI datasets and one CT dataset. Experimental results demonstrate that the proposed method strikes a splendid balance between enhancement and denoising, providing performance superior to that of several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Haze during bad weather degrades visibility of the scene drastically. Degradation of the scene visibility varies concerning the transmission map (TrMap) of the scene. Estimation of accurate TrMap is a key step to reconstruct the haze-free scene. Previous approaches, DehazeNet (Cai et al. in IEEE Trans Image Process 25(11):5187-5198, 2016), MSCNN (Ren et al., in: European conference on computer vision, Springer, New York, 2016), overcome the drawbacks of handcrafted features. However, they fail to recover the haze-free scene without color distortion. In this paper, cardinal color (red, green and blue) fusion network is proposed to estimate the robust TrMap. Proposed network recovers haze-free scene without color distortion. In the first stage, it generates multi-channel depth maps using a novel parallel convolution filter bank. The second stage comprises of multi-scale convolution filter bank which extracts the scale-invariant features. Further, spatial pooling followed by convolution filter estimates the robust scene transmission map. In the last stage, estimated TrMap is given to the optical model to recover the haze-free scene. Three benchmark datasets namely D-HAZY (Ancuti et al., in: 2016 IEEE international conference on image processing (ICIP), 2016), ImageNet (Deng et al., in: IEEE conference on computer vision and pattern recognition, 2009 (CVPR 2009), 2009) and a set of real-world hazy scenes are used to analyze the effectiveness of the proposed approach. Structural similarity index, mean square error and peak signal-to-noise ratio are used to evaluate the proposed network for single image haze removal. Further, the effect of haze on object detection is analyzed and robust cascaded pipeline, i.e., haze removal followed by Faster RCNN is proposed for object detection in the hazy environment. Performance analysis shows that the proposed network estimates robust TrMap and recovers haze-free scene without color distortion. [ABSTRACT FROM AUTHOR]

Abstract Restoring hazy images is challenging since it must account for several physical factors that are related to the image formation process. Existing analytical methods can only provide partial solutions because they rely on assumptions that may not be valid in practice. This research presents an effective method for restoring hazy images based on genetic programming. Using basic mathematical operators several computer programs that estimate the medium transmission function of hazy scenes are automatically evolved. Afterwards, image restoration is performed using the estimated transmission function in a physics-based restoration model. The proposed estimators are optimized with respect to the mean-absolute-error. Thus, the effects of haze are effectively removed while minimizing overprocessing artifacts. The performance of the evolved GP estimators given in terms of objective metrics and a subjective visual criterion, is evaluated on synthetic and real-life hazy images. Comparisons are carried out with state-of-the-art methods, showing that the evolved estimators can outperform these methods without incurring a loss in efficiency, and in most scenarios achieving improved performance that is statistically significant. [ABSTRACT FROM AUTHOR]

State-of-the-art methods for removing haze from a single image rely on a haze image formation model and the inversion problem is solved by estimating medium transmission and global atmospheric light. In this paper, we propose a hazy image enhancement framework, called Intensity Mapping and Detail Mapping(IMDM), for dehazing, which can strike a good balance in enhancing details and preserving color fidelity. We propose two mapping fucntions to respectively process the detail and intensity components of the hazy image instead of jointly tackling the two parts. Compared with joint tackling of these two kinds of information, respective processings are more advantagous in stretching the contrast and highlight the details. For obtaining the optimal parameters of mapping functions, we specially set up an image dataset, which consists of numbers of pairs of hazy and the corresponding ground truth images, for the training process. In order to simulate hazy images in the real world as much as possible, hazy images in this dataset are captured in an artificial hazy environment. With the learned parameters, details in the hazy image can be clearly restored without causing color infidelity. Experimental results demonstrate that the proposed method (called IMDM) is superior to both the enhancement and model-based methods in terms of improving the visibility and preserving color fidelity. [ABSTRACT FROM AUTHOR]

Image haze removal has been extensively studied, but there has been no such an image database regarding the haze level. It is not convenient for readers to verify the assumptions or priors that are supposed to be useful for haze removal, and meanwhile, it is not fair to compare the performance of haze removal methods, which are effective for images with different haze levels. To solve this problem, we built a database consisting of more than 3464 images of different kinds of outdoor scenes. The images of the database are grouped into four classes regarding the haze level. Along with the database, we also observe a frequency magnitude prior, i.e. the frequency magnitude decreases with the increasing haze level, which can be used as a prior to develop haze removal methods. Our purpose is to help develop image haze removal methods, as well as verify existing statistical priors and discover new ones that can be used for image processing. [ABSTRACT FROM AUTHOR]

Image haze removal techniques are extensively used in several outdoor applications. Lack of sufficient knowledge that is required to restore hazy images, the existing techniques usually use various attributes and assign constant values to these attributes. Unsuitable assignment to these attributes does not provide desired dehazing results. The primary objective of this review paper is to provide a structured outline of some well-known haze removal techniques. This paper also focuses on the methods which can assign optimal values to image dehazing attributes. The review has revealed that the meta-heuristic techniques can attain the optimistic haze removal parameters and also concurrently develops an optimistic objective function to estimate the depth map efficiently. Finally, this paper describes the various issues and challenges of image dehazing techniques, which are required to be further studied. [ABSTRACT FROM AUTHOR]

Haze removal has become an attractive topic in recent years and several dehazing methods are proposed. Dark channel prior (DCP) is one of the most effective dehazing approaches. However, when dealing with images containing large white objects, DCP often mistakes white objects for opaque haze. It will cause the airlight to be overestimated and the transmission to be underestimated, and thus the dehazing results have serious color distortion. In view of the above problem, saliency detection is introduced into haze removal to obtain better restored images in this paper. We first propose a method for reliable airlight estimation. Then, a saliency prior is presented for hazy images, which can distinguish white objects from dense haze by saliency detection. On the basis of saliency prior, both accurate airlight and a correct transmission map can be obtained from images containing large white objects, and finally these images can be restored successfully. The experimental results illustrate that our proposed method has great superiority in color recovery compared with other state-of-art methods when dealing with images containing large white objects. ©20i8SPiEandIS&T[DDI: 10.1117/1. JEI.27.2.023038] [ABSTRACT FROM AUTHOR]

Optical satellite imagery is often contaminated by the persistent presence of clouds and atmospheric haze. Without an effective method for removing this contamination, most optical remote sensing applications are less reliable. In this research, a methodology has been developed to fully automate and improve the Haze Optimized Transformation (HOT)-based haze removal. The method is referred to as AutoHOT and characterized with three notable features: a fully automated HOT process, a novel HOT image post-processing tool and a class-based HOT radiometric adjustment method. The performances of AutoHOT in haze detection and compensation were evaluated through three experiments with one Landsat-5 TM, one Landsat-7 ETM+ and eight Landsat-8 OLI scenes that encompass diverse landscapes and atmospheric haze conditions. The first experiment confirms that AutoHOT is robust and effective for haze detection. The average overall, user’s and producer’s accuracies of AutoHOT in haze detection can reach 96.4%, 97.6% and 97.5%, respectively. The second and third experiments demonstrate that AutoHOT can not only accurately characterize the haze intensities but also improve dehazed results, especially for brighter targets, compared to traditional HOT radiometric adjustment. [ABSTRACT FROM AUTHOR]

Haze removal is a nontrivial work for medium-altitude unmanned aerial vehicle (UAV) image processing because of the effects of light absorption and scattering. The challenges are attributed mainly to image distortion and detail blur during the long-distance and large-scale imaging process. In our work, a metadataassisted nonuniform atmospheric scattering model is proposed to deal with the aforementioned problems of medium-altitude UAV. First, to better describe the real atmosphere, we propose a nonuniform atmospheric scattering model according to the aerosol distribution, which directly benefits the image distortion correction. Second, considering the characteristics of long-distance imaging, we calculate the depth map, which is an essential clue to modeling, on the basis of UAV metadata information. An accurate depth map reduces the color distortion compared with the depth of field obtained by other existing methods based on priors or assumptions. Furthermore, we use an adaptive median filter to address the problem of fuzzy details caused by the global airlight value. Experimental results on both real flight and synthetic images demonstrate that our proposed method outperforms four other existing haze removal methods. [ABSTRACT FROM AUTHOR]

Satellite remote sensing image often suffers from haze degradation, which deteriorates significantly the effect of data intelligibility and interpretability. Hence, haze removal technique is essential for inferring high quality images with clear visibility to improve the application value of the satellite images. Thin haze removal is a challenging task since the estimation of haze component is easily affected by ground features. To solve the problem, this paper develops an effective haze removal method for a single visible remote sensing image. Firstly, haze is considered as an additive contamination and can be represented by a haze thickness map (HTM). A ground radiance suppressed HTM (GRS-HTM) is then proposed for a more precise estimation of haze distribution. The haze component for each band is calculated via GRS-HTM and can be removed to recover the clear image. Several visible satellite images with different resolutions were tested to validate the effectiveness of the proposed method. The evaluation results with qualitative and quantitative assessments demonstrate that the proposed method is superior to the traditional methods, and can recover a haze-free image with high quality. [ABSTRACT FROM AUTHOR]

To improve the image quality and compensate deficiencies of haze removal, we presented a novel fusion method. By analyzing the darkness channel of each method, the effective darkness channel model that takes the correlation information of each darkness channel into account was constructed. This method was used to estimate the transmission map of the input image, and refined by the modified guided filter in order to further improve the image quality. Finally, the radiance image was restored by combining the monochrome atmospheric scattering model. Experimental results show that the proposed method not only effectively remove the haze of the image, but also outperform the other haze removal methods. [ABSTRACT FROM AUTHOR]

In this paper, a novel method named Haze Removal based on Two Steps (HRTS) for removing the haze has been proposed based on two steps, which obviously improves the image qualities such as color and visibility caused by haze. The proposed method mainly consists of two steps: the preprocessing step by decomposing the input image to reduce the influence of ambient light and the removed haze step for restoring the radiance. We first reduce the effect of the ambient light by decomposing the haze image, estimate the transmission map based on the result of the decomposition, and then use the modified guided filter method to refine it. Finally, the monochrome atmospheric scattering model is combined to restore the radiance image. Experimental results show that the proposed method could effectively remove the haze and obviously improve the color and visibility of the image in the realistic scenes by comparing other existing haze removal methods. [ABSTRACT FROM AUTHOR]

To address the gloomy sky and the low contrast caused by the left fog in the existing image dehazing methods, we propose a robust haze removal algorithm for images and videos. First, a sky detection-based adaptive atmospheric light estimation method is designed for brighter and cleaner restoration results for the sky regions. Second, in order to reconstruct a transmission map in line with the depth variation, we preprocess the input image with texture smoothing to keep the color consistency inside the same planar object and devise a texture smoothing-based robust transmission estimation method, with which the contrast and color saturation of fog-free image are greatly promoted. Finally, the restored results are post-processed with the joint bilateral filter for the purpose of noise removal. What's more, a guided filter-based temporally coherent atmospheric light smoothing strategy and a Gaussian filter-based spatial-temporally coherent transmission smoothing strategy are put forward for video dehazing, which can ensure the spatial as well as temporal continuity of the haze-free videos. Experimental results show that the recovered haze-free images and videos have high contrast and color saturation with cleaner sky regions, and the haze-free videos are free of jittering and flickering phenomena. [ABSTRACT FROM AUTHOR]

Image haze removal has become an important research direction in the field of computer vision because of the vast development and increasing demand of its applications. Outdoor images that are captured in bad weather are degraded due to factors like noise and haze. These factors seriously affect the visibility of the image. Images may contain impulse noise which is produced by the sensor and circuitry of image- capturing devices like cameras. Images may also contain haze, which is formed due to the combination of two fundamental phenomena namely, attenuation and the airlight. Attenuation reduces the image contrast and air light increases the whiteness in the image, thereby making the images unclear. This work presents a hybrid approach for denoising and dehazing a single noisy and hazy image. First, the input image is passed through an adaptive median filter to remove the impulse noise. Then the resultant image is dehazed using simple color attenuation prior. The experimental results show that the visual quality of the output images are much better than the original input images, which proves the efficiency of this method. [ABSTRACT FROM AUTHOR]

Image haze removal has become an important research direction in the field of computer vision because of the vast development and increasing demand of its applications. In the present era, where uploading images in social media has become very common, the image editing and processing applications has gained much popularity. Outdoor images that are captured in bad weather are seriously degraded due to several factors. Haze, being the most common degrading factor, affects the visibility of the images and makes them unclear. Haze is formed due to a combination of two fundamental phenomena namely, attenuation and airlight. Attenuation deteriorates the scene contrast and air light increases the whiteness in the scene. Therefore, the removal of attenuation and airlight helps to restore the color and contrast of the images, making them haze-free. Different works have been proposed till date inorder to filter out haze from images. This paper analyses the different image haze removal techniques. The different techniques, along with their merits and demerits are discussed. [ABSTRACT FROM AUTHOR]

Haze removal is useful in computational photography and computer vision applications. Although many haze removal algorithms have been proposed, their computational efficiency requires improvement. A real-time haze removal method is presented in this paper. The method is based on the concept of a dark channel prior. To enhance the haze removal performance, an approximate method to estimate the atmospheric light and transmission is employed. For embedded system applications, a hardware architecture to perform real-time haze removal is proposed. The hardware can achieve 116 MHz on Stratix FPGA. The simulation results indicate that the hardware is highly efficient and performs well. It obtains good image recovery results and satisfies the real-time requirement even for large images. [ABSTRACT FROM PUBLISHER]

• We propose a hybrid network, which employs two sub-networks to handle haze removal and image details refinement, respectively. Each sub-network has its own contributions to enhance image. One sub-network is used to reconstruct the basic dehazed image and the other one further enhances the contrast and details of the dehazed result. The whole dehazing framework adopts multi-term losses to optimize model, and this dehazing process also can be treated as a coarse-to-fine manner. • To obtain basic dehazed result, we combine the Squeeze-and-Excitation (SE) with residual learning to aggregate more spatial contextual information, which can be used to remove basic haze components. To preserve more image details, we utilize dilated convolution operation to obtain larger fields and design multi-scale dilated layers to conduct image details refinement. • Our method can effectively remove complex haze and preserve more image details. Particularly, the detail refinement sub-network can be detached into other existing SID methods to improve their dehazing performance. Experimental results substantial improvements both on quantitative indicators and visual effects over the current state-of-the-art technologies. Besides, the proposed method can promote other computer vision tasks, such like object detection. Deep learning technologies have been applied in Single Image Dehazing (SID) tasks successfully. However, most SID algorithms seldom consider to refine image details during dehazing. Therefore, there exist some detail-loss regions in dehazed results. To solve this issue, we design a deep hybrid network to improve dehazing performance and remedy the loss of details. Different from existing algorithms that usually ignore detail refinement and adopt a unified framework to remove haze, we propose to treat dehazing and detail refinement as two separate tasks, so that each task could be solved via different ways. Particularly, we design two sub-networks with a multi-term loss function. First, for removing haze effectively, we introduce the Squeeze-and-Excitation (SE) to design a haze residual attention sub-network, which is used to reconstruct the dehazed image. Second, as for remedying details, we take the previous dehazed image as the input to a detail refinement sub-network, where the image details can be enhanced via multi-scale contextual information aggregation. Through the joint training of two sub-network, the haze can be removed clearly and the image details can be preserved well. Moreover, the detail refinement sub-network can be detached into other existing dehazing methods to improve their model performances. Extensive experiments also verify the superiority of our proposed network against recently proposed state-of-the-arts. [ABSTRACT FROM AUTHOR]

An enhanced radiographic image usually seems blurred. In order to sharpen the enhanced image, a novel image processing method is proposed that makes the enhanced image clearer. After redefining the dark channel of the radiographic images, the procedure of the haze removal method is presented. In this method, the original image, the enhanced image and the haze removal image can be taken as a natural scene on dark, foggy and sunny days, respectively. The comparative experiments and quantitative analysis prove that this method can improve the image quality. Thus, this method can be taken as an effective post-processing method for the enhanced image. [ABSTRACT FROM AUTHOR]

A high-quality underwater image is essential to many industrial and academic applications in the field of image processing and analysis. Unfortunately, underwater images frequently demonstrate poor visual quality of low contrast, blurring, darkness, and color diminishing. This paper develops a new underwater image restoration framework that consists of four major phases: color correction, local contrast enhancement, haze diminution, and global contrast enhancement. A self-adaptive mechanism is designed to guide the image to either processing route based on a red deficiency measure. In the color correction phase, the histogram in each RGB channel is transformed for balancing the image color. An adaptive histogram equalization method is exploited to enhance the local contrast in the CIE-Lab color space. The dark channel prior haze removal scheme is modified for dehazing in the haze diminution phase. Finally, a histogram stretching method is applied in the HSI color space to make the image more natural. A wide variety of underwater images with various scenarios were employed to evaluate this new restoration algorithm. Experimental results demonstrated the effectiveness of our image restoration scheme as compared with state-of-the-art methods. It was suggested that our framework dramatically eliminated the haze and improved visual interpretation of underwater images. [ABSTRACT FROM AUTHOR]