Alternating minimization algorithm for hybrid regularized variational image dehazing

Shu, Qiaoling; Wu, Chuansheng; Zhong, Qiuxiang; Liu, Ryan Wen

doi:10.1016/j.ijleo.2019.04.002

Cited by 21 publications

(12 citation statements)

References 28 publications

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“…Shu et al [30] introduced an effective two-step transmission map estimation model, which uses a hybrid variational regularization model to refine the transmission and fuses two different transmission maps to obtain the sharp image. In [31], a hybrid regularized variational framework is proposed, which introduces the secondorder total generalized variation regularizer to constrain the estimation of a depth map. Recently, combining with the reconstruction of haze model and two effective priors, Fang et al [32] proposed a variational model in the Y channel of YUV color space for image dehazing.…”

Section: A Single Image Dehazing Methodsmentioning

confidence: 99%

A Mixed Transmission Estimation Iterative Method for Single Image Dehazing

et al. 2021

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Considering the frequent occurrence of hazy weather, single image dehazing has become an important research task. Physical model-based and deep learning-based methods are two competitive methods in single image dehazing, but achieving fidelity and effectively removing haze at the same time in real hazy scenes is still a challenging problem. In this work, we propose a mixed iterative model to restore high-quality clear images by integrating a physical model-based approach and a learning-based approach, which can combine the advantages of the physical model-based method in maintaining natural attributes and the advantages of the learning-based method in completely removing haze. For a hazy image, first, according to the haze density, it is divided into separate regions to calculate the local atmospheric light. Then, we utilize the dark channel prior and DehazeNet to jointly estimate the transmission, which can estimate an accurate recovered image that is more in line with the real scene. Finally, a numerical iterative strategy is employed to further optimize the atmospheric light and transmission. The experiments on both synthetic and natural hazy image datasets indicate that the proposed method can restore natural and clean images and has superior performance than other state-of-the-art algorithms in peak signal-to-noise ratio, structural similarity, information entropy, color natural index, and color histogram. And in the four indicators of the quantitative results of the synthetic hazy test set, we achieve the highest score in three of them and the second highest result in another. In the comparison of the two quantitative indicators of natural hazy images, our average objective results are both the highest.INDEX TERMS Image dehazing, numerical iterative, mixed model, joint transmission.

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Section: A Single Image Dehazing Methodsmentioning

confidence: 99%

A Mixed Transmission Estimation Iterative Method for Single Image Dehazing

et al. 2021

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“…e loss function is used to preserve the structural information significantly. Shu et al [31] used a multichannel total variation (MTV) regularizer to restore the hazy images. e alternating direction approach of multipliers is utilized for a nonsmooth optimization problem.…”

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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“…Recently, pixel clustering in the RGB space was used to reduce image artifacts during image defogging assuming that colors in a haze-free image are closely approximated by a few hundreds of distinct colors [13]. Furthermore, some optimization approaches have been proposed for obtaining enhanced dehazing results [14], [15]. For instance, some methods based on Artificial Intelligence (AI) have yielded to promising results through the use of a Multilayer Perceptron (MLP) [16].…”

Section: Introductionmentioning

confidence: 99%