Rw-Dm: Retinex and wavelet-based diffusion model for low-light image enhancement in underground coal mines
Abstract The images in underground coal mines suffer from low contrast and poor visibility. While diffusion model-based approaches for low-light image enhancement have shown considerable promise, these methods are often characterized by high resource consumption, slow processing speeds, and a tenden...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-06-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-025-01966-x |
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| _version_ | 1849331456374996992 |
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| author | Yadong Li Jun Tian Yang Chen Hongdong Wang Hui Yan Yang Peng Tianjiao Wang |
| author_facet | Yadong Li Jun Tian Yang Chen Hongdong Wang Hui Yan Yang Peng Tianjiao Wang |
| author_sort | Yadong Li |
| collection | DOAJ |
| description | Abstract The images in underground coal mines suffer from low contrast and poor visibility. While diffusion model-based approaches for low-light image enhancement have shown considerable promise, these methods are often characterized by high resource consumption, slow processing speeds, and a tendency towards instability. To address these challenges, this paper presents a diffusion model that integrates Retinex and Wavelet transform (RW-DM). This methodology harnesses the generative capabilities of diffusion models to produce visually appealing outcomes in the enhancement of low-light images. This approach decomposes low-light images into reflectance and illumination components, utilizing the advantages of wavelet transformation to significantly enhance processing speed and reduce computational resource consumption while preserving data integrity. Additionally, the study introduces a Multiple Residual Module (MRM) to preserve details in both the decomposing network (MR-Decompose) and the denoising network (MRU-Denoise). Extensive experiments on low-light images from coal mines demonstrate that RW-DM outperforms existing state-of-the-art methods qualitatively. Moreover, the application of the proposed method in pedestrian detection using underground coal mine images resulted in a 3.1% increase in $$\hbox {mAP}_{50}$$ mAP 50 . This improvement not only showcases the effectiveness of the proposed approach but also highlights its potential practical value. |
| format | Article |
| id | doaj-art-2925248ac43f4e2a87d2628997100aaf |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-2925248ac43f4e2a87d2628997100aaf2025-08-20T03:46:34ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-06-0111811710.1007/s40747-025-01966-xRw-Dm: Retinex and wavelet-based diffusion model for low-light image enhancement in underground coal minesYadong Li0Jun Tian1Yang Chen2Hongdong Wang3Hui Yan4Yang Peng5Tianjiao Wang6School of Information and Control Engineering, China University of Mining and TechnologySchool of Information and Control Engineering, China University of Mining and TechnologySchool of Electrical Engineering, China University of Mining and TechnologySchool of Information and Control Engineering, China University of Mining and TechnologySchool of Intelligent Engineering, Jiangsu Vocational College of Information TechnologySchool of Low-Carbon Energy and Power Engineering, China University of Mining and TechnologySchool of Electrical Engineering and Automation, Jiangsu Normal UniversityAbstract The images in underground coal mines suffer from low contrast and poor visibility. While diffusion model-based approaches for low-light image enhancement have shown considerable promise, these methods are often characterized by high resource consumption, slow processing speeds, and a tendency towards instability. To address these challenges, this paper presents a diffusion model that integrates Retinex and Wavelet transform (RW-DM). This methodology harnesses the generative capabilities of diffusion models to produce visually appealing outcomes in the enhancement of low-light images. This approach decomposes low-light images into reflectance and illumination components, utilizing the advantages of wavelet transformation to significantly enhance processing speed and reduce computational resource consumption while preserving data integrity. Additionally, the study introduces a Multiple Residual Module (MRM) to preserve details in both the decomposing network (MR-Decompose) and the denoising network (MRU-Denoise). Extensive experiments on low-light images from coal mines demonstrate that RW-DM outperforms existing state-of-the-art methods qualitatively. Moreover, the application of the proposed method in pedestrian detection using underground coal mine images resulted in a 3.1% increase in $$\hbox {mAP}_{50}$$ mAP 50 . This improvement not only showcases the effectiveness of the proposed approach but also highlights its potential practical value.https://doi.org/10.1007/s40747-025-01966-xDeep learningLow-light image enhancementMulti-Scale feature extractionIntegration of Retinex and Wavelet transform |
| spellingShingle | Yadong Li Jun Tian Yang Chen Hongdong Wang Hui Yan Yang Peng Tianjiao Wang Rw-Dm: Retinex and wavelet-based diffusion model for low-light image enhancement in underground coal mines Complex & Intelligent Systems Deep learning Low-light image enhancement Multi-Scale feature extraction Integration of Retinex and Wavelet transform |
| title | Rw-Dm: Retinex and wavelet-based diffusion model for low-light image enhancement in underground coal mines |
| title_full | Rw-Dm: Retinex and wavelet-based diffusion model for low-light image enhancement in underground coal mines |
| title_fullStr | Rw-Dm: Retinex and wavelet-based diffusion model for low-light image enhancement in underground coal mines |
| title_full_unstemmed | Rw-Dm: Retinex and wavelet-based diffusion model for low-light image enhancement in underground coal mines |
| title_short | Rw-Dm: Retinex and wavelet-based diffusion model for low-light image enhancement in underground coal mines |
| title_sort | rw dm retinex and wavelet based diffusion model for low light image enhancement in underground coal mines |
| topic | Deep learning Low-light image enhancement Multi-Scale feature extraction Integration of Retinex and Wavelet transform |
| url | https://doi.org/10.1007/s40747-025-01966-x |
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