MFHOD: Multi-modal image fusion method based on the higher-order degradation model

Guo, Jinxin; Zhan, Weida; Jiang, Yichun; Ge, Wei; Chen, Yu; Xu, Xiaoyu; Li, Jin; Liu, Yanyan

doi:10.1016/j.eswa.2024.123731

Cited by 6 publications

(2 citation statements)

References 54 publications

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“…-βd(x) t(x)=e (5) In Equation ( 6), 𝛽 represents the scattering coefficient of the atmosphere. This formula indicates that as the depth of the scene increases, the brightness of the scene decreases exponentially.…”

Section: De-fogging Algorithm Based On Multi-channel Threshold Inspec...mentioning

confidence: 99%

“…Consequently, in the current field of digital image processing, image dehazing has gained extensive research attention as a critical method for enhancing image clarity 3,4 . Because atmospheric disturbances, such as haze, smoke, and dust, are prevalent in most scenes and often severely affect image quality and visual performance, they can result in loss of image details, color distortion, and reduced contrast 5 .…”

Section: Introductionmentioning

confidence: 99%

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TMCD: Threshold multi-channel prior-based defogging algorithm for underground coal mine images

Fan,

Mao,

et al. 2024

Preprint

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With the rapid development of image dehazing algorithms, the demand for effective dehazing solutions across various industries has markedly increased. However, the application effectiveness of most existing image dehazing algorithms within coal mine environments remains suboptimal. Consequently, this paper proposes an image dehazing algorithm based on a threshold multi-channel inspection method. The algorithm detects fog density using an enhanced color attenuation prior method, followed by image enhancement in fog-free areas and dehazing in foggy areas through threshold multi-channel inspection. During fog density detection, the algorithm incorporates texture information and illumination invariance features from the HSV space, enhancing adaptability and robustness to different lighting conditions. In the dehazing process, segregating foggy and fog-free images facilitates more accurate and reliable dehazing outcomes. Moreover, by constructing a multi-scale pyramid and employing a guided filtering approach, the algorithm achieves more precise estimation of the image transmittance, mitigates the blocky artifacts common in traditional methods. For video dehazing, a parameter reuse mechanism based on inter-frame similarity is designed, improving the real-time performance of video dehazing. The algorithm was tested on a coal mine dataset and on partial public datasets such as NH-Haze2 and Dense-Haze, achieving experimental results that surpass other algorithms.

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Section: De-fogging Algorithm Based On Multi-channel Threshold Inspec...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TMCD: Threshold multi-channel prior-based defogging algorithm for underground coal mine images

Fan,

Mao,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

TQ-cGAN: A Trible-Generator Quintuple-Discriminator Conditional Generative Adversarial Network for multimodal grayscale medical image fusion

Deng,

Han

2025

Biomedical Signal Processing and Control

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AFDFusion: An adaptive frequency decoupling fusion network for multi-modality image

Wang,

Zhao,

Yang

et al. 2025

Expert Systems with Applications

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MFHOD: Multi-modal image fusion method based on the higher-order degradation model

Cited by 6 publications

References 54 publications

TMCD: Threshold multi-channel prior-based defogging algorithm for underground coal mine images

TMCD: Threshold multi-channel prior-based defogging algorithm for underground coal mine images

TQ-cGAN: A Trible-Generator Quintuple-Discriminator Conditional Generative Adversarial Network for multimodal grayscale medical image fusion

AFDFusion: An adaptive frequency decoupling fusion network for multi-modality image

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