Haze-free Image Research Articles

Real-time haze removal in monocular images using locally adaptive processing

This research presents the design of a real-time system to remove the effects of haze in a sequence of monocular images. The system firstly estimates the medium transmission function from an observed hazy image using locally adaptive neighborhoods and calculation of order statistics. Next, the haze-free image is retrieved using the estimated transmission function and a physics-based restoration model. The performance of the proposed system is evaluated and compared with that of similar existing techniques in terms of objective metrics. The obtained results exhibit that the proposed system yields a higher performance in comparison with tested similar methods. Because of its high computational efficiency, the proposed system is able to operate at high rate and it is suitable for real-time applications.

Fast Image Dehazing Method Based on Linear Transformation

Images captured in hazy or foggy weather conditions are seriously degraded by the scattering of atmospheric particles, which directly influences the performance of outdoor computer vision systems. In this paper, a fast algorithm for single image dehazing is proposed based on linear transformation by assuming that a linear relationship exists in the minimum channel between the hazy image and the haze-free image. First, the principle of linear transformation is analyzed. Accordingly, the method of estimating a medium transmission map is detailed and the weakening strategies are introduced to solve the problem of the brightest areas of distortion. To accurately estimate the atmospheric light, an additional channel method is proposed based on quad-tree subdivision. In this method, average grays and gradients in the region are employed as assessment criteria. Finally, the haze-free image is obtained using the atmospheric scattering model. Numerous experimental results show that this algorithm can clearly and naturally recover the image, especially at the edges of sudden changes in the depth of field. It can, thus, achieve a good effect for single image dehazing. Furthermore, the algorithmic time complexity is a linear function of the image size. This has obvious advantages in running time by guaranteeing a balance between the running speed and the processing effect.

Dehazed Image Quality Assessment by Haze-Line Theory

Images captured in bad weather suffer from low contrast and faint color. Recently, plenty of dehazing algorithms have been proposed to enhance visibility and restore color. However, there is a lack of evaluation metrics to assess the performance of these algorithms or rate them. In this paper, an indicator of contrast enhancement is proposed basing on the newly proposed haze-line theory. The theory assumes that colors of a haze-free image are well approximated by a few hundred distinct colors, which form tight clusters in RGB space. The presence of haze makes each color cluster forms a line, which is named haze-line. By using these haze-lines, we assess performance of dehazing algorithms designed to enhance the contrast by measuring the inter-cluster deviations between different colors of dehazed image. Experimental results demonstrated that the proposed Color Contrast (CC) index correlates well with human judgments of image contrast taken in a subjective test on various scene of dehazed images and performs better than state-of-the-art metrics.

Haze Removal via Edge Weighted Pixel-to-Patch Fusion

One of the key issues in image dehazing is how to accurately estimate the transmission map using strong priors or assumptions. By far the most common prior adopted by existing haze removal approaches is dark channel prior (DCP). Despite the remarkable progress, the existing DCP-based methods induce misestimation of the transmission while preventing halo artifacts, and may cause distortions of the recovered haze-free images in both chromaticity and contrast. This because they fail to observe the fact that the edges in a color image do not necessarily correspond to the changes in depth; instead, they try to preserve all types of edges indiscriminately in the estimated transmission map, which does not accord with the property of a realistic transmission map. In order to address this issue, we propose an efficient method for haze removal under the guidance of the depth edges. The main contribution of our study is that we present a depth edge prior to obtain the depth edges from the hazy image, and then employ a pixel-to-patch fusion scheme weighted by the depth edges to estimate the transmission directly, which can preserve the sharp discontinuity at depth edges but smooth the surface texture in the rest regions of the transmission map. The experimental results show that our approach can obtain more accurate estimation of the transmission, and consequently restore better quality haze-free images in terms of less color distortion and high contrast.

Dehazing of remote sensing images using improved restoration model based dark channel prior

ABSTRACTHaze degrades visual information of remotely sensed images. Therefore, haze removal is a demanding and significant task for visual multispectral information improvement. The existing haze removal techniques utilize different restrictions and before restoring hazy images in an efficient manner. The review of existing haze removal methods demonstrates that the haze-free images suffer from colour distortion and halo artefacts problems. To solve these issues, an improved restoration model based dark channel prior is proposed in this paper. The proposed technique has redefined transmission map, with the aim to reduce the colour distortion problem. The modified joint trilateral filter is also utilized to improve the coarse estimated atmospheric veil. The experimental results reveal that the proposed approach provides visually significant haze-free images and also preserves the significant detail.

Instant haze removal from a single image

Images captured in bad weather are usually be degraded compared to those in clear days. For this, we draw on atmospheric optics and we propose a simple but powerful approach to remove haze from a single image based on imaging model under atmospheric scattering. For images with nearby objects under dense fog or distant objects under mild fog, we estimate the scene structure as transmission from the image model based on the optical irradiance characteristic which depends solely on its depth. Moreover, we present a reliable statistical principle for estimating global atmospheric light. Incorporating these two estimations into the haze imaging model, we can recover the contrast and color quality as well as that of a haze-free image. Experimental results on a variety of hazy images have shown the power of the methodology.

Dehazing for images with large sky region

This paper introduces a haze removal algorithm based on region decomposition and features fusion to overcome the challenges of the dark channel prior-based algorithm, such as block effect and color distortion. In our proposed method, an image is decomposed with the quad-tree method based on gradient and grayscale information to obtain the sky regions. These sky regions are used as the seed point for region-growing, which will segment the image into sky and non-sky regions. A Gaussian filter is applied for smoothing on the segmented image, which is then used as a weight map to optimize the transmission image in the dark channel prior algorithm. Finally, the haze-free images are obtained based on an atmospheric scattering model and color compensation. Our experimental results demonstrated that images restored using this algorithm are generally clear and natural, and the algorithm is especially suitable for hazy images with large sky regions.

Single image dehazing by latent region‐segmentation based transmission estimation and weighted L 1 ‐norm regularisation

Image dehazing is a useful technique which can eliminate the bad effect of haze on images and enhance the performances of image/video processing algorithms in the hazy weather. In this study, a single image dehazing method is proposed. The authors estimate the initial transmission properly based on latent region-segmentation and refine the estimated initial transmission by an objective function with a novel weighted L 1-norm regularisation term. The half-quadratic splitting minimisation method is employed to solve this optimisation problem. They also define an evaluation function to estimate the reliable global atmospheric light. With the refined transmission map and atmospheric light they recover the haze-free image by the haze imaging model. The authors’ method is compared with three state-of-the-art methods and is also validated by two image quality assessment methods. The comparative experimental results and evaluations demonstrate that their method can recover comparable and even better results with clear details, low contrast loss and high contrast in most cases.

Haze removal for a single visible remote sensing image

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.

Single Image Dehazing via Large Sky Region Segmentation and Multiscale Opening Dark Channel Model

Outdoor images acquired under poor weather conditions are usually contaminated by suspended particles and aerosols in the atmosphere. These captured images easily suffer from contrast reduction, low visibility, and color distortion. In this paper, we develop a novel single image dehazing method based on large sky region segmentation and multiscale opening dark channel model (MODCM). First, a simple but effective method for large sky region detection based on SVM classification is presented, which can be considered as the first step of atmospheric light estimation. Then, two different strategies are utilized for obtaining a more accurate estimate of the atmospheric light according to the mentioned detection result. Furthermore, MODCM can adaptively make use of different patch sizes to calculate the dark channel according to different edge levels, which can prevent halo artifacts near edges of depth discontinuity. In addition, the gradient domain guided filter is adopted to refine the initial transmission map due to its accuracy near edges. Finally, the haze-free image can be obtained through correcting the colors of the sky region and combining the sky and non-sky region. Experimental results on different kinds of hazy images indicate that our proposed approach can produce the visually desirable results with genuine color and high scene visibility, even superior than the other state-of-the-art dehazing methods.

Single Image Dehazing Based on Combining Dark Channel Prior and Scene Radiance Constraint

Images captured in foggy or hazy weather conditions often suffer from poor visibility. The dark channel prior method has well solved the single image dehazing problem in nature, but it is invalid when the scene objects are inherently similar to the atmospheric light and no shadow is cast on them. We propose an efficient regularization method by adding a scene radiance constraint and combing the dark channel prior to remove hazes from a single input image. The experiments show that this improved algorithm can deal with various levels of foggy weather conditions, as well as greatly enhance the image's visibility and details. In addition, the recovered haze-free image has little or no halo artifacts.

DehazeNet: An End-to-End System for Single Image Haze Removal.

Single image haze removal is a challenging ill-posed problem. Existing methods use various constraints/priors to get plausible dehazing solutions. The key to achieve haze removal is to estimate a medium transmission map for an input hazy image. In this paper, we propose a trainable end-to-end system called DehazeNet, for medium transmission estimation. DehazeNet takes a hazy image as input, and outputs its medium transmission map that is subsequently used to recover a haze-free image via atmospheric scattering model. DehazeNet adopts convolutional neural network-based deep architecture, whose layers are specially designed to embody the established assumptions/priors in image dehazing. Specifically, the layers of Maxout units are used for feature extraction, which can generate almost all haze-relevant features. We also propose a novel nonlinear activation function in DehazeNet, called bilateral rectified linear unit, which is able to improve the quality of recovered haze-free image. We establish connections between the components of the proposed DehazeNet and those used in existing methods. Experiments on benchmark images show that DehazeNet achieves superior performance over existing methods, yet keeps efficient and easy to use.

Haze removal based on multiple scattering model with superpixel algorithm

An efficient method to remove haze from single image based on dark channel prior and the multiple scattering described by atmosphere point spread function (APSF) is proposed in this paper. By means of statistical analysis of the quality assessments for more than 100 images, we apply generalized Gaussian distribution to approximate APSF kernel in the image domain with parameter mapping from both shape similarity and numeric reasonability. Furthermore, a superpixel method is employed for estimating the transmission on the sky and non-sky region, in order to mitigate the halo artifact around the sharp edges and reduce color distortion in the sky region. Therefore, the haze-free images with abundant distinguished details and little halo artifacts can be finally restored by first applying deconvolution to the original hazy image. A series of experiments are additionally implemented to demonstrate the effectiveness and robustness of the proposed dehazing algorithm from both qualitative and quantitative comparisons with the state-of-art.

A Review on Methods of Image Dehazing

Literature survey is an important for understanding and gaining much more knowledge about the specific area of a subject. The outdoor images captured in inclement weather are degraded due to the presence of haze, fog, rain and so on. Images of scenes captured in bad weather have poor contrasts and colors. This may cause difficulty in detecting the objects in the captured hazy images. Due to haze there is a trouble to many computer vision applications as it diminishes the visibility of the scene. This paper presents a study about different image dehazing methods to remove the haze from the hazy images captured in real world weather conditions to recover a fast and improved quality of haze free images. There is a improvement in terms of contrast, visible range and color fidelity. All these techniques are widely used in many applications such as outdoor Surveillance, object detection, underwater images, etc. General Terms Bad weather conditions, Haze, Airlight, Direct attenuation, contrast, color fidelity, Haze model.

Image Haze Removal Using Dark Channel Prior and Inverse Image

Image haze removal using dark channel prior is prone to encountering color distortion in sky and brightness region. To solve the problem, we proposed an improved method based on inverse image and dark channel prior. Firstly, we applied inverse image to estimating a new transmission map. The new transmission map can be used to modify original transmission map in order to avoid color distortion. Next, fast guider filter was applied to refining transmission map. Using the transmission map, we can recover a high quality haze-free image. Experimental results showed that the proposed method is feasible, and visibility can be enhanced.

안개 제거 기술의 정량적인 성능 평가 기법 조사

Image dehazing has been extensively studied, but the performance evaluation method for dehazing techniques has not attracted significant interest. This paper surveys many existing performance evaluation methods of image dehazing. In order to analyze the reliability of the evaluation methods, synthetic hazy images are first reconstructed using the ground-truth color and depth image pairs, and the dehazed images are then compared with the original haze-free images. Meanwhile we also evaluate dehazing algorithms not by the dehazed images' quality but by the performance of computer vision algorithms before/after applying image dehazing. All the aforementioned evaluation methods are analyzed and compared, and research direction for improving the existing methods is discussed.

Single image dehazing based on vector quantization

This paper presents a method for the dehazing of images through the application of the atmosphere model associated with McCartney Optics. The proposed method is based on the assumption that similarities among hazy blocks are associated with similarities in transmissions. A coarse transmission map was fabricated using vector quantization and then refined using a guided image filter. Subtracting the effects of atmospheric light from hazy images makes it possible to obtain haze-free images. Experiment results demonstrate that the proposed method produces images that appear more natural than those obtained using other methods.

A Review on Fog Removal with Its Techniques and Types

Now days, Images play essentialrole in real world, images defines not only the changes in environment and also use for traffic analysis. But images are captured in open environment due to the bed whether or atmosphere images are not too good. This paper review on the different haze removal processes to eliminate the haze in the captured hazy images to recover a better and improved quality haze free images [1].

Edge-Preserving Decomposition-Based Single Image Haze Removal.

Single image haze removal is under-constrained, because the number of freedoms is larger than the number of observations. In this paper, a novel edge-preserving decomposition-based method is introduced to estimate transmission map for a haze image so as to design a single image haze removal algorithm from the Koschmiedars law without using any prior. In particular, weighted guided image filter is adopted to decompose simplified dark channel of the haze image into a base layer and a detail layer. The transmission map is estimated from the base layer, and it is applied to restore the haze-free image. The experimental results on different types of images, including haze images, underwater images, and normal images without haze, show the performance of the proposed algorithm.

SINGLE IMAGE DEHAZING FOR VISIBILITY IMPROVEMENT

Abstract. Images captured in foggy weather conditions often suffer from poor visibility, which will create a lot of impacts on the outdoor computer vision systems, such as video surveillance, intelligent transportation assistance system, remote sensing space cameras and so on. In this paper, we propose a new transmission estimated method to improve the visibility of single input image (with fog or haze), as well as the image’s details. Our approach stems from two important statistical observations about haze-free images and the haze itself. First, the famous dark channel prior, a statistics of the haze-free outdoor images, can be used to estimate the thickness of the haze; and second, gradient prior law of transmission maps, which is based on dark channel prior. By integrating these two priors, to estimate the unknown scene transmission map is modeled into a TV-regularization optimization problem. The experimental results show that the proposed approach can effectively improve the visibility and keep the details of fog degraded images in the meanwhile.