A Novel Total Generalized Variation Model for Image Dehazing

Gu, Yanan; Yang, Xiaoping; Gao, Yiming

doi:10.1007/s10851-019-00909-9

Cited by 12 publications

(4 citation statements)

References 16 publications

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“…Also, DCP [15] and CTT [51] suffer from the sky-region problem. CNN [52] and TGV [56] provide better results, but still some texture distortion is there. WT [53], L 1 norm [55], and FVID [54] suffer from some halo and gradient-reversal artifacts.…”

Section: Visual Analyses Of Proposed Dehazing Modelmentioning

confidence: 99%

“…ese approaches are DCP [50], CTT [51], CNN [52], WT [53], FVID [54], L 1 norm [55], and TGV [56]. Fifteen benchmark synthetic and real-life hazy images are considered for experimental analysis.…”

Section: Performance Analysismentioning

confidence: 99%

“…Figure 3: Results of dehazing models: (a) input image, (b) DCP [50], (c) CNN [52], (d) CTT[51], (e) TGV[56], (f ) WT[53], (g) L 1 norm[55], (h) FVID[54], and (i) proposed BCP model.…”

mentioning

confidence: 99%

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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: Visual Analyses Of Proposed Dehazing Modelmentioning

confidence: 99%

Section: Performance Analysismentioning

confidence: 99%

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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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show abstract

“…A third approach is the variational approach where parameters are determined by minimizing a functional derived from different priors. For example in [5] the generalized total variation prior is used, based in the assumption that the transmission map is piecewise smooth. In [6] the prior used was built upon ideas from computational color constancy, namely gray world assumption, which will also be used in this paper.…”

Section: Prior Workmentioning

confidence: 99%

Local Gray World Method for Single Image Dehazing

Stipetić¹,

Lončarić²

2020

8th International Conference on Signal, Image Processing and Pattern Recognition

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Images taken outdoors are often degraded by atmospheric conditions such as fog and haze. These degradations can reduce contrast, blur edges, and reduce saturation of images. In this paper we propose a new method for single image dehazing. The method is based on an idea from color constancy called the gray world assumption. This assumption states that the average values of each channel in a picture are the same. Using this assumption and a haze degradation model we can quickly and accurately estimate the haze thickness and recover a haze free image. The proposed method is validated on a synthetic and natural image dataset and compared to other methods. The experimental results have shown that the proposed method provides comparable results to other dehazing methods.

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BPFD-Net: enhanced dehazing model based on Pix2pix framework for single image

Lin

Yang

et al. 2021

Machine Vision and Applications

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A Novel Total Generalized Variation Model for Image Dehazing

Cited by 12 publications

References 16 publications

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

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

Local Gray World Method for Single Image Dehazing

BPFD-Net: enhanced dehazing model based on Pix2pix framework for single image

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