A Robust Method for Dehazing of Single Image with Sky Region Detection and Segmentation

International Journal of Image and Graphics ◽

10.1142/s0219467821500455 ◽

2021 ◽

pp. 2150045

Author(s):

Tannistha Pal

Keyword(s):

Quality Improvement ◽

Dark Channel Prior ◽

Robust Method ◽

Single Image ◽

Image Dehazing ◽

Region Detection ◽

Color Distortion ◽

Tremendous Progress ◽

Dark Channel ◽

Computer Vision Applications

In recent times, there has been a tremendous progress in image dehazing for computer vision applications, while the sky region processed by these algorithms tends to degrade by noise and color distortion. In this paper, an improved dark channel prior algorithm is proposed which detects the sky region first and divides the image into sky region and non-sky region and then estimates the transmission of two parts separately, followed by combining with refining step. The proposed algorithm also accurately corrects the transmission of the sky region to avoid noise and color distortion. Experimental results show a greater quality improvement in the output images than the existing strategies.

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Adaptive Tolerance Dehazing Algorithm Based on Dark Channel Prior

Algorithms ◽

10.3390/a13020045 ◽

2020 ◽

Vol 13 (2) ◽

pp. 45

Author(s):

Fan Yang ◽

ShouLian Tang

Keyword(s):

Estimation Algorithm ◽

Third Party ◽

Dark Channel Prior ◽

Single Image ◽

Tolerance Mechanism ◽

Image Dehazing ◽

Bright Region ◽

Color Distortion ◽

Direct Light ◽

Dark Channel

The tolerance mechanism based on dark channel prior (DCP) of a single image dehazing algorithm is less effective when there are large areas of the bright region in the hazy image because it cannot obtain the tolerance adaptively according to the characteristics of the image. It will lead to insufficient improvement of the transmission of image, so it is difficult to eliminate the color distortion and block effects in the restored image completely. Moreover, when a dense haze area or a third-party direct light source (sunlight, headlights and reflected glare) is misjudged as sky area, the use of tolerance will cause an inferior dehazing effect such as details lost. Regarding the issue above, this paper proposes an adaptive tolerance estimation algorithm. The tolerance is obtained according to the statistic characteristics of each image to make the estimation of transmission more accurately. The experimental results show that the proposed algorithm not only maintains high operational efficiency but also effectively compensates for the defects of the dark channel prior to some scenes. The proposed algorithm can effectively solve the problem of color distortion recovered by the DCP method in the bright regions of the image.

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Single Image Dehazing with V-transform and Dark Channel Prior

Journal of Systems Science and Information ◽

10.21078/jssi-2020-185-10 ◽

2020 ◽

Vol 8 (2) ◽

pp. 185-194

Author(s):

Xiaochun Wang ◽

Xiangdong Sun ◽

Ruixia Song

Keyword(s):

Median Filter ◽

Color Space ◽

Low Frequency ◽

Dark Channel Prior ◽

Single Image ◽

Image Dehazing ◽

Color Distortion ◽

Dark Channel ◽

Reconstruction Image ◽

Single Image Dehazing

AbstractSingle image dehazing algorithm based on the dark channel prior may cause block effect and color distortion. To improve these limitations, this paper proposes a single image dehazing algorithm based on the V-transform and the dark channel prior, in which a hazy RGB image is converted into the HSI color space, and each component H, I and S is processed separately. The hue component H remains unchanged, the saturation component S is stretched after being denoised by a median filter. In the procession of intensity component, a quad-tree algorithm is presented to estimate the atmospheric light, the dark channel prior and the V-transform are used to estimate the transmission map. To reduce the computational complexity, the intensity component I is decomposed by the V-transform first, coarse transmission map is then estimated by applying the dark channel prior on the low frequency reconstruction image, and the guided filter is finally employed to refine the coarse transmission map. For images with sky regions, the haze removal effectiveness can be greatly improved by just increasing the minimum value of the transmission map. The proposed algorithm has low time complexity and performs well on a wide variety of images. The recovered images have more nature color and less color distortion compared with some state-of-the-art methods.

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A Fast Single-Image Dehazing Algorithm Based on Dark Channel Prior and Rayleigh Scattering

IEEE Access ◽

10.1109/access.2020.2988144 ◽

2020 ◽

Vol 8 ◽

pp. 73330-73339 ◽

Cited By ~ 1

Author(s):

Jehoiada Jackson ◽

She Kun ◽

Kwame Obour Agyekum ◽

Ariyo Oluwasanmi ◽

Parinya Suwansrikham

Keyword(s):

Rayleigh Scattering ◽

Dark Channel Prior ◽

Single Image ◽

Image Dehazing ◽

Dark Channel ◽

Single Image Dehazing

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An image dehazing algorithm based on double priors constraint

MATEC Web of Conferences ◽

10.1051/matecconf/201818904009 ◽

2018 ◽

Vol 189 ◽

pp. 04009

Author(s):

Kun Liu ◽

Shiping Wang ◽

Linyuan He ◽

Duyan Bi ◽

Shan Gao

Keyword(s):

Standard Deviation ◽

Mean Value ◽

Experimental Results ◽

Dark Channel Prior ◽

Image Dehazing ◽

Color Distortion ◽

Local Media ◽

Dark Channel

Aiming at the color distortion of the restored image in the sky region, we propose an image dehazing algorithm based on double priors constraint. Firstly, we divided the haze image into sky and non-sky regions. Then the Color-lines prior and dark channel prior are used for estimating the transmission of sky and non-sky regions respectively. After introducing color-lines prior to correct sky regions restored by the dark channel prior, we get an accurate transmission. Finally, the local media mean value and standard deviation are used to refine the transmission to obtain the dehazing image. Experimental results show that the algorithm has obvious advantages in the recovery of the sky area.

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