An Image Dehazing Method Based On an Improved Retinex Theory

Alharbi, Ebtesam Mohameed; Shan, Yilin; Ge, Peng; Wang, Hong

doi:10.2991/iccia-16.2016.21

Cited by 3 publications

(8 citation statements)

References 19 publications

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“…The backscattering of atmospheric particles on natural light leads to image contrast reduction, hue deviation, and image saturation [19]. These back and forward scattering effects on sensor light and natural light in hazy images are widely modeled through a dark channel prior model as follows [1,[3][4][5]:…”

Section: A Materials 1) Atmospheric Scattering Modelmentioning

confidence: 99%

“…Existing haze removal strategies are based on image enhancement, and image restoration [4]. Image enhancement methods focus on improving the image quality without considering the physical model of the imaging principle [5]. Such methods include histogram equalization [6], retinex theory [7], and saliency ex-traction [8].…”

Section: Introductionmentioning

confidence: 99%

“…The further degradation of the hazy image by image enhancement methods result from incomplete recovery effects or color distortion [1]. Image restoration methods focus on image recovery based on a physical model comprising atmospheric scattering [5,9,10]. These image recovery methods rely on auxiliary or prior information about the image [5].…”

Section: Introductionmentioning

confidence: 99%

“…Image restoration methods focus on image recovery based on a physical model comprising atmospheric scattering [5,9,10]. These image recovery methods rely on auxiliary or prior information about the image [5]. The complexity of image recovery based on prior information makes this method not preferable by many researchers [5].…”

Section: Introductionmentioning

confidence: 99%

“…These image recovery methods rely on auxiliary or prior information about the image [5]. The complexity of image recovery based on prior information makes this method not preferable by many researchers [5]. Haze removal methods (called supplementary) that combine traditional techniques with other image enhancement levels, such as histogram homogenization and wavelet analysis, have also been reported [4].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Geometric-Pixel Guided Single-Pass Convolution Neural Network With Graph Cut for Image Dehazing

Alenezi

Ganesan

2021

IEEE Access

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One of the major shortcomings of existing image dehazing algorithms is in estimating scene transmittance, which has assumed many items in the existing algorithms. One key assumption has been pixel uniformity and smoothness. In this paper, we propose to solve the dehazing problem using a combination of single-pass CNN with graph cut algorithms. It considers the transmittance based on differential pixel-based variance, local and global patches and energy functions to improve the transmission map. The proposed algorithm was tested on different images and evaluated based on various evaluation metrics. Our results show more details when compared to four existing benchmark enhancement methods. The proposed method has one major drawback: the over-bright areas tend to lose some features in the final image.

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Section: A Materials 1) Atmospheric Scattering Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Geometric-Pixel Guided Single-Pass Convolution Neural Network With Graph Cut for Image Dehazing

Alenezi

Ganesan

2021

IEEE Access

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A Novel Image Dehazing Algorithm via Adaptive Gamma-Correction and Modified AMEF

Zhuang

Zou

et al. 2020

IEEE Access

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Captured images are usually influenced by fog or haze. In reality, image dehazing is challenging. This paper proposes a modified artificial multiple-exposure image fusion (AMEF) algorithm to remove the haze from an image. In the algorithm, first, an adaptive gamma-correction transform with the mean and standard deviation values of each component of a hazy image is utilized to verify the intensities. Second, the homomorphic filtering algorithm is introduced into the Gaussian pyramid and Laplacian pyramid to compute the exposed accessible images. Last, a modified Laplacian filter method is presented to calculate the contrast of the exposed accessible images. Further, extensive experimental results demonstrate that the proposed algorithm has superior performance compared with that of some state-of-the-art methods, including higher contrast, richer details and a better visual effect in the dehazed image.INDEX TERMS Image dehazing, adaptive gamma-correction transformation, image fusion, Laplacian pyramid, modified AMEF.

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Local Defogging Algorithm for Improving Visual Impact in Image Based on Multiobjective Optimization

Qiu-ju

2022

Mathematical Problems in Engineering

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The preprocessing of images is required for many applications based on industry, social, and academic requirements. Researchers have developed a number of techniques to improve the visual effect of images and appropriately interpret visual effects. The accuracy of visuals is important in cyber security, military organization, police organizations, and forensics to detect the true story from the pictures. They search for evidence by digging deep into the network in search of evidence. If visuals are not clear, preprocessing of images is not done correctly, then it may lead to wrong interpretations. This paper proposes an image local defogging technique based on multiobjective optimization to improve the visual effect of the image as well as the information entropy. The multiobjective function is selected to establish the image reconstruction model based on multiple objectives. The model is utilized to reconstruct a single image to moderate the impact of noise and other interference factors in the original image. The color constancy model and effective detail intensity model are also devised for image enhancement to get the visual details. The atmospheric light value and transmittance are evaluated using a physical model of atmospheric scattering, and the guided filter is used to maximize the transmittance of a single image and improve the efficiency of image defogging. The dark channel priority method is used to realize the local defogging of a single image and to design the local defogging algorithm. Experiments verify the optimization effect of the proposed algorithm in terms of information entropy and container network interface (CNI) value. The tone restoration degree is good, and it improves the overall image quality. The image defogging effect of the proposed algorithm is verified with respect to subjective and objective levels to check the efficacy of the proposed multiobjective model.

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An Image Dehazing Method Based On an Improved Retinex Theory

Cited by 3 publications

References 19 publications

Geometric-Pixel Guided Single-Pass Convolution Neural Network With Graph Cut for Image Dehazing

Geometric-Pixel Guided Single-Pass Convolution Neural Network With Graph Cut for Image Dehazing

A Novel Image Dehazing Algorithm via Adaptive Gamma-Correction and Modified AMEF

Local Defogging Algorithm for Improving Visual Impact in Image Based on Multiobjective Optimization

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