Image dehazing using adaptive bi-channel priors on superpixels

Jiang, Yutong; Sun, Changming; Zhao, Yu; Li, Yang

doi:10.1016/j.cviu.2017.10.014

Cited by 59 publications

(28 citation statements)

References 19 publications

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“…Therefore, a range of post-processing methods [29][30][31] are followed by a transmission estimation. Superpixel-based approaches [32,33] are an alternative for reducing a negative artifact when applying DCP to the images.…”

Section: Transmission Map Estimationmentioning

confidence: 99%

Underwater Image Enhancement Using Successive Color Correction and Superpixel Dark Channel Prior

2020

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Underwater images generally suffer from quality degradations, such as low contrast, color cast, blurring, and hazy effect due to light absorption and scattering in the water medium. In applying these images to various vision tasks, single image-based underwater image enhancement has been challenging. Thus, numerous efforts have been made in the field of underwater image restoration. In this paper, we propose a successive color correction method with a minimal reddish artifact and a superpixel-based restoration using a color-balanced underwater image. The proposed successive color correction method comprises an effective underwater white balance based on the standard deviation ratio, followed by a new image normalization. The corrected image based on this color balance algorithm barely produces a reddish artifact. The superpixel-based dark channel prior is exploited to enhance the color-corrected underwater image. We introduce an image-adaptive weight factor using the mean of backscatter lights to estimate the transmission map. We perform intensive experiments for various underwater images and compare the performance of the proposed method with those of 10 state-of-the-art underwater image-enhancement methods. The simulation results show that the proposed enhancement scheme outperforms the existing approaches in terms of both subjective and objective quality.

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Section: Transmission Map Estimationmentioning

confidence: 99%

Underwater Image Enhancement Using Successive Color Correction and Superpixel Dark Channel Prior

2020

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“…However, DCP suffers from a number of problems such as sky-region, halo, and gradient-reversal artifacts, color, edges, and texture distortion issues [16]. Recently, researchers have proposed various channel priors to handle the issues associated with standard DCP such as boosting dark channel [17], bounded optimization-based dark channel prior [18], gradient channel prior [19], adaptive bichannel priors [20], sparse dark channel prior [21], and dark channel prior guided variational framework [22]. However, the existing methods perform poorly especially when images contain a large haze gradient.…”

Section: Introductionmentioning

confidence: 99%

Efficient Single Image Dehazing Model Using Metaheuristics-Based Brightness Channel Prior

Kehar

Chopra²,

Singh

et al. 2021

Mathematical Problems in Engineering

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Haze degrades the spatial and spectral information of outdoor images. It may reduce the performance of the existing imaging models. Therefore, various visibility restoration models approaches have been designed to restore haze from still images. But restoring the haze is an open area of research. Although the existing approaches perform significantly better, they are not so effective against a large haze gradient. Also, the effect of hyperparameters tuning issue is also ignored. Therefore, a brightness channel prior (BCP) based dehazing model is proposed. The gradient filter is utilized to improve the transmission map computed using the gradient filter. Nondominated Sorting Genetic Algorithm is also used to optimize the initial parameters of the BCP approach. The comparative analysis shows that BCP performs effectively across a wide range of haze degradation levels without causing any visible artifacts.

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“…Though, this approach is not valid if the brightness of the scene similar to the atmospheric light. Another kind of solution recommended in [8], [9], [10][11][12][13][14], [15][16][17], and [18][19] based on multiscale retinex technique, pixel-based dark channel prior, filtering based approach, prior knowledge, and RGB to HSI color space conversion respectively. In [20][21], estimated unknown transmission map by employing the boundary constraint and learning procedure with the help of Random Forest [22] for an efficient regularization.…”

Section: Introductionmentioning

confidence: 99%

Fast and Efficient Visibility Restoration Technique for Single Image Dehazing and Defogging

Kumari¹,

Sahoo

Chinnaiah³

2021

IEEE Access

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Poor weather conditions are detrimental to transportation systems and increase the likelihood of road accidents. Haze and fog are the most responsible atmospheric parameters affecting visibility and hence the traffic performance. The intelligent driving assistance systems developed for automatic vehicles use clear vision for various smart applications like keeping within the correct lane and recognize traffic signs. Bad weather decreases the visibility significantly based on the intensity of fog and haze. So, there is a need for the restoration of clear visibility. This paper introduces a novel visibility restoration approach based on thresholding and gamma transformation method. A step-by-step process for the proposed method is as follows. First, the proper selection of an atmospheric light value is responsible for the validation of the color and contrast of the recovered images. The threshold process makes it possible to estimate atmospheric light. Then, accurately estimating the transmission depth from object to object in small time is the most challenging aspect due to unequal distribution. To solve the problem, the gamma transformation method is used to correctly estimate the depth. Finally, restore the scene radiance from the hazy and foggy images. The experimental results show that the proposed measurement ensures good uniformity about qualitative and quantitative evaluation using eight performance metrics: contrast gain, percentage of saturated pixels, blind contrast assessment, structural similarity index measure (SSIM), image visibility measurement (IVM), mean square error (MSE), visual contrast measure (VCM), and peak signal-to-noise ratio (PSNR). The proposed algorithm excels in previous works in terms of processing time and visibility restoration results comparable to sophisticated state-of-the-art techniques.INDEX TERMS Atmospheric light, transmission map, gamma correction, performance metrics, processing time.

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Image dehazing using adaptive bi-channel priors on superpixels

Cited by 59 publications

References 19 publications

Underwater Image Enhancement Using Successive Color Correction and Superpixel Dark Channel Prior

Underwater Image Enhancement Using Successive Color Correction and Superpixel Dark Channel Prior

Efficient Single Image Dehazing Model Using Metaheuristics-Based Brightness Channel Prior

Fast and Efficient Visibility Restoration Technique for Single Image Dehazing and Defogging

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