An image fusion dehazing algorithm based on dark channel prior and Retinex

Zhang, Wei; Zhu, Guangli; Liu, Wenjuan; Liang, Xingzhu

doi:10.1504/ijcse.2020.10032223

Cited by 3 publications

(3 citation statements)

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“…Wei [21] (2017) proposed a new image dehazing method based on DCP theory and the interval interpolation wavelet transform for the degradation of image quality collected in hazy weather conditions.…”

Section: Introductionmentioning

confidence: 99%

AgriGAN: Unpaired image dehazing via A Cycle-Consistent Generative Adversarial Network for the Agricultural Plant Phenotype

Ding,

Peng,

Huang

et al. 2024

Preprint

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Artificially extracted agricultural phenotype information has high subjectivity and low accuracy, and the use of image extraction information is easily disturbed by haze. Moreover, the agricultural image dehazing method used to extract such information is ineffective, as the images often contain unclear texture information and image colors. To address these shortcomings, we propose unpaired image dehazing via a cycle-consistent generative adversarial network for the agricultural plant phenotype (AgriGAN). The algorithm improves the dehazing performance of the network by adding the atmospheric scattering model, which improves the discriminator model, and uses the whole-detail consistent discrimination method to improve the efficiency of the discriminator so that the adversarial network can accelerate the convergence to the Nashi equilibrium state. Finally, the dehazed images are obtained by training with network adversarial loss + cycle consistent loss. Experiments and a comparative analysis were conducted to evaluate the algorithm, and the results show that it improved the dehazing accuracy of agricultural images, retained detailed texture information, and mitigated the problem of color deviation. In turn, useful information was obtained, such as crop height, chlorophyll and nitrogen content, and the presence and extent of disease. The algorithm's object identification and information extraction can be useful in crop growth monitoring and yield and quality estimation.

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Section: Introductionmentioning

confidence: 99%

AgriGAN: Unpaired image dehazing via A Cycle-Consistent Generative Adversarial Network for the Agricultural Plant Phenotype

Ding,

Peng,

Huang

et al. 2024

Preprint

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show abstract

“…There are many improved algorithms based on these three algorithms [ 20 , 21 , 22 ]. A single haze removal algorithm usually struggles to solve the problems of haze removal and detail preservation, so some scholars have fused image restoration and image enhancement methods to give full play to the advantages of various algorithms [ 23 , 24 , 25 ]. To protect geometric details while filtering, He et al further proposed the guided filtering theory [ 26 , 27 ], and some scholars have applied this theory to image haze removal [ 28 , 29 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Image Haze Removal Method Based on Histogram Gradient Feature Guidance

Huang

Zhang

2023

IJERPH

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Optical remote sensing images obtained in haze weather not only have poor quality, but also have the characteristics of gray color, blurred details and low contrast, which seriously affect their visual effect and applications. Therefore, improving the image clarity, reducing the impact of haze and obtaining more valuable information have become the important aims of remote sensing image preprocessing. Based on the characteristics of haze images, combined with the earlier dark channel method and guided filtering theory, this paper proposed a new image haze removal method based on histogram gradient feature guidance (HGFG). In this method, the multidirectional gradient features are obtained, the atmospheric transmittance map is modified using the principle of guided filtering, and the adaptive regularization parameters are designed to achieve the image haze removal. Different types of image data were used to verify the experiment. The experimental result images have high definition and contrast, and maintain significant details and color fidelity. This shows that the new method has a strong ability to remove haze, abundant detail information, wide adaptability and high application value.

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“…It is often very difficult for a single algorithm to obtain images that can not only effectively eliminate haze, but also obtain better contrast and clear details. Therefore, some scholars usually fuse image restoration and image enhancement methods, which can give full play to the advantages of different methods [23][24][25][26]. The third class is the deep learning theory and related algorithms, which are also used in image haze removal [1,[27][28][29][30][31][32][33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Image haze removal based on rolling deep learning and Retinex theory

Huang

Liu

et al. 2021

IET Image Processing

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Multispectral remote sensing images are a very important data source, but its acquisition process is often affected by haze weather and other factors, resulting in the decline of image quality, blurred details and poor visual effect, which seriously affect their application and interpretation. To reduce the impact of haze on multispectral remote sensing image and improve the image clarity and the use value, a new haze removal method based on rolling deep learning and Retinex theory (RDLRT) was proposed here. It uses the rolling deep learning theory to realize the preliminary removal of image haze, and then calculates the peak signal-to-noise ratio (PSNR) value of the processed image, which is used to judge whether the obtained image is a stage effect image (SEI). Then, on the one hand, the SEI image is processed for colour saturation and brightness; on the other hand, the Retinex theory is used to further filter the haze of the SEI image. Finally, the results of the two processing are fused, and it is used as the restored image after removing the haze. A series of validation experiments were carried out with true multispectral remote sensing images, outdoor colour images and different methods, and good experimental results were obtained. The purpose of removing image haze and improving image quality is achieved. Through the comparison of these experiments, it is concluded that the RDLRT algorithm proposed in this paper can not only effectively remove haze in images, but also maintain good details and colour of the image, which has some popularization values for image pre-processing and restoration.

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An image fusion dehazing algorithm based on dark channel prior and Retinex

Cited by 3 publications

References 0 publications

AgriGAN: Unpaired image dehazing via A Cycle-Consistent Generative Adversarial Network for the Agricultural Plant Phenotype

AgriGAN: Unpaired image dehazing via A Cycle-Consistent Generative Adversarial Network for the Agricultural Plant Phenotype

Image Haze Removal Method Based on Histogram Gradient Feature Guidance

Image haze removal based on rolling deep learning and Retinex theory

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