Real-time framework for image dehazing based on linear transmission and constant-time airlight estimation

Alajarmeh, Ahmad; Salam, Rosalina Abdul; Abdulrahim, Khairi; Marhusin, Mohd Fadzli; Zaidan, A. A.; Zaidan, B. B.

doi:10.1016/j.ins.2018.01.009

Cited by 20 publications

(5 citation statements)

References 20 publications

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“…e global image contrast is enhanced by using the haze veil map. Alajarmeh et al [36] proposed an image restoration model based on contrast time airlight and liner transmission (CLT). Two approaches are used such as airlight by image integrals to estimate the airlight value and bounded transmission to estimate the linear transmission maps.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…Consequently, the aforementioned filters can transfer the guidance image’s structures to the filtering output while retaining the linear run-time complexity. Similar to Tarel and Hautiere [ 16 ], Alajarmeh et al [ 21 ] devised a dehazing framework with linear run-time complexity by virtue of the constant-time atmospheric light and linear-time transmittance estimation. Nevertheless, this method is prone to the color shift problem and incorrect selection of atmospheric light, as demonstrated by the bluish sky in their reported results.…”

Section: Literature Reviewmentioning

confidence: 99%

Automating a Dehazing System by Self-Calibrating on Haze Conditions

Ngo

Lee

et al. 2021

Sensors

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Existing image dehazing algorithms typically rely on a two-stage procedure. The medium transmittance and lightness are estimated in the first stage, and the scene radiance is recovered in the second by applying the simplified Koschmieder model. However, this type of unconstrained dehazing is only applicable to hazy images, and leads to untoward artifacts in haze-free images. Moreover, no algorithm that can automatically detect the haze density and perform dehazing on an arbitrary image has been reported in the literature to date. Therefore, this paper presents an automated dehazing system capable of producing satisfactory results regardless of the presence of haze. In the proposed system, the input image simultaneously undergoes multiscale fusion-based dehazing and haze-density-estimating processes. A subsequent image blending step then judiciously combines the dehazed result with the original input based on the estimated haze density. Finally, tone remapping post-processes the blended result to satisfactorily restore the scene radiance quality. The self-calibration capability on haze conditions lies in using haze density estimate to jointly guide image blending and tone remapping processes. We performed extensive experiments to demonstrate the superiority of the proposed system over state-of-the-art benchmark methods.

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“…A gradient algorithm is used to solve the problem of constrained optimization. Alajarmeh and Salam [31] proposed a method to recover smog-free images using estimation of constant-time airlight and linear transmission.…”

Section: Literature Reviewmentioning

confidence: 99%

Image smog restoration using oblique gradient profile prior and energy minimization

Kumar

Jain

2021

Front. Comput. Sci.

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Removing the smog from digital images is a challenging pre-processing tool in various imaging systems. Therefore, many smog removal (i.e., desmogging) models are proposed so far to remove the effect of smog from images. The desmogging models are based upon a physical model, it means it requires efficient estimation of transmission map and atmospheric veil from a single smoggy image. Therefore, many prior based restoration models are proposed in the literature to estimate the transmission map and an atmospheric veil. However, these models utilized computationally extensive minimization of an energy function. Also, the existing restoration models suffer from various issues such as distortion of texture, edges, and colors. Therefore, in this paper, a convolutional neural network (CNN) is used to estimate the physical attributes of smoggy images. Oblique gradient channel prior (OGCP) is utilized to restore the smoggy images. Initially, a dataset of smoggy and sunny images are obtained. Thereafter, we have trained CNN to estimate the smog gradient from smoggy images. Finally, based upon the computed smog gradient, OGCP is utilized to restore the still smoggy images. Performance analyses reveal that the proposed CNN-OGCP based desmogging model outperforms the existing desmogging models in terms of various performance metrics. Electronic Supplementary Material Supplementary material is available in the online version of this article at 10.1007/s11704-020-9305-8.

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Real-time framework for image dehazing based on linear transmission and constant-time airlight estimation

Cited by 20 publications

References 20 publications

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

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

Automating a Dehazing System by Self-Calibrating on Haze Conditions

Image smog restoration using oblique gradient profile prior and energy minimization

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