Efficient Image Dehazing with Boundary Constraint and Contextual Regularization

Meng, Guang; Wang, Ying; Duan, Jiangyong; Xiang, Shiming; Chen, Pan

doi:10.1109/iccv.2013.82

Cited by 975 publications

(900 citation statements)

References 15 publications

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“…16a, c; the corresponding quantitative comparison results are listed in Table 1. As Table 1 shows, Tarel [10] achieves the maximum e value, followed by Meng [12], Gibson [15], and He [13]. However, this does not mean these algorithms are superior to our method, because the number of visible edges can increase when excessive dehazing leads to noise amplification in the image.…”

Section: B Imentioning

confidence: 93%

“…14, we compare our method with the algorithms presented by Taral [10], Meng [12], He [13], Gibson [15], Zhu [17], and Qi [27]. Obviously, the sky color is overenhanced in the results of Taral, Meng, He, Gibson and Qi, while it is not in our method and Zhu's; however, our method outperforms Zhu's algorithm in the dehazing visual effect.…”

Section: Comprehensive Comparisonmentioning

confidence: 96%

“…To solve this problem, Xiao [11] proposed a guided joint bilateral filter for haze removal. Meng [12] estimated a rough transmission map using a boundary constraint and proposed a regularization method to blur the map. Although this method is fast, it tends to distort the color fidelity when dealing with white objects.…”

Section: Introductionmentioning

confidence: 99%

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Single image dehazing via an improved atmospheric scattering model

Wang

2016

Vis Comput

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Under foggy or hazy weather conditions, the visibility and color fidelity of outdoor images are prone to degradation. Hazy images can be the cause of serious errors in many computer vision systems. Consequently, image haze removal has practical significance for real-world applications. In this study, we first analyze the inherent weaknesses of the atmospheric scattering model and propose an improvement to address those weaknesses. Then, we present a fast image haze removal algorithm based on the improved model. In our proposed method, the input image is partitioned into several scenes based on the haze thickness. Next, averaging and erosion operations calculate the rough scene luminance map in a scene-wise manner. We obtain the rough scene transmission map by maximizing the contrast in each scene and then develop a way to gently remove the haze using an adaptive method for adjusting scene transmission based on scene features. In addition, we propose a guided total variation model for edge optimization, so as to prevent from the block effect as well as to eliminate the negative effect from the wrong scene segmentation results. The experimental results demonstrate that our method is effective in solving a series of common problems, including uneven illuminance, overenhanced and oversaturated images, and so forth. Moreover, our method outperforms most current dehazing algorithms in terms of visual effects, universality, and processing speed.

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Section: B Imentioning

confidence: 93%

Section: Comprehensive Comparisonmentioning

confidence: 96%

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Single image dehazing via an improved atmospheric scattering model

Wang

2016

Vis Comput

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“…Accept that this haze demonstrate is direct model. From the meaning of linearity in this model [6] just pixel position is changed. This intangibility is happened by two basic: Direct attenuation and Air light.…”

Section: Saturation Mapmentioning

confidence: 99%

“…Where J a color channel of J, µ is is a mean and σ is a standard deviation of r, g, b intensity values and Ω(x) is a nearby patch focused at x.We consider top 1% pixels in registering airlight and discover the pixel which has most extreme estimation of J in its dull channel among the pixels in light of equation (6). The estimation of I at that pixel is considered as airlight.…”

Section: A Dark Channel Priormentioning

confidence: 99%

A Fast Method for Haze Evacuation in Remote Sensing Images by Using Gaussian Filter

Reddy

2018

IJRASET

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The perceivability of the open air images is degraded on account of terrible climate conditions. Robust solutions to this issue, a simple and novel strategy to take out the haze from satellite remote sensing images using Gaussian filter. The task is challenging due to variations in dark channel prior, air light, transmission map and radiance map. The nearness of haze in the atmosphere degrades the quality of images captured by visible camera sensors. The expulsion of haze is commonly performed under the statistical strategies. Due to haziness, an image generally lost shading and edges. So de hazing strategy restores edge losses and shading impacts seriously. The strategy plays out a per-pixel operation, which is direct to create develop and then apply the Gaussian filter to improve the image quality. Exploratory outcomes show that the proposed strategy can permit a quick execution, it is viable for visual engaging and shading loyalty contrasted with some state-of-the-art methods.

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An efficient approach for removing haze from single image using Gaussian pyramidal decomposition

Pandian¹,

Poruran²

2021

Concurrency and Computation

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The visibility of the image is degraded by haze components which are affected in image during image acquisition process. The haze removal process can be categorized into haze reduction from single image and haze reduction from multiple images. The second case consumes more time and the complexity of algorithm is high. In order to overcome such limitations, this paper proposes an efficient methodology for haze reduction in single acquired image. In this paper, haze detection and removing methodology is proposed using Gaussian pyramidal decomposition. This proposed methodology is tested on both indoor and outdoor haze affected images. The performance of this proposed haze removal algorithm is analyzed with respect to average running time and peak signal to noise ratio, mean square error, mean absolute error, Entropy, and normalized histogram intersection coefficient. K E Y W O R D Sdecomposition, Gaussian pyramidal, haze detection, single image INTRODUCTIONImage surveillance and object detection in an image plays an important role in real world applications. During the acquiring of the images, hazy and foggy particles will present in the image which degrades the quality of the acquired images. The lights scattered by these particles will also affect the luminance intensity of the nearby pixels in an image. [1][2][3][4][5] This will create fading and low contrast pixels in an image. Hence, the visibility of the image is degraded with respect to increasing these scattered components in outdoor or indoor environments. This will create many problems for real time image processing applications such as unmanned vehicle moving system and satellite image system. 6,7 Hence, there is a need for removing the haze components from the acquired image for improving the quality of the image. This will help the computer vision based applications in real time image processing applications. 8 Figure 1(A) shows the haze affected in outdoor images and Figure 1(B) shows the haze affected in indoor images.The performance of computer vision algorithms and advanced image editing algorithms can also be improved. Therefore, haze removal is highly demanded in image processing, computational photography and computer vision applications. Since the amount of scattering depends on the unknown distances of the scene points from the camera and the air-light is also unknown, it is challenging to remove haze from haze images, especially when there is only a single haze image. Many methods were presented for detecting and removing the haze components by using multiple images. [9][10][11][12][13] This process required complex algorithm for removing the haze from the image using multiple images. Also, the average time consumption for removing the haze component from the single image by multiple images is high. In order to overcome such limitations in conventional haze removing process, this paper proposes an efficient methodology for removing the haze components from the single source image only. This method uses Gaussian pyramidal decomposition (GPD) a...

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Efficient Image Dehazing with Boundary Constraint and Contextual Regularization

Cited by 975 publications

References 15 publications

Single image dehazing via an improved atmospheric scattering model

Single image dehazing via an improved atmospheric scattering model

A Fast Method for Haze Evacuation in Remote Sensing Images by Using Gaussian Filter

An efficient approach for removing haze from single image using Gaussian pyramidal decomposition

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