Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
Low-light image enhancement is crucial for accurately interpreting images captured under low-lighting conditions. Existing low-light enhancement methods based on diffusion models have demonstrated effectiveness; however, they suffer from slow training processes and less structured guidance during op...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/3/1604 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850068294029017088 |
|---|---|
| author | Li Li Jishen Peng Yingcai Wan |
| author_facet | Li Li Jishen Peng Yingcai Wan |
| author_sort | Li Li |
| collection | DOAJ |
| description | Low-light image enhancement is crucial for accurately interpreting images captured under low-lighting conditions. Existing low-light enhancement methods based on diffusion models have demonstrated effectiveness; however, they suffer from slow training processes and less structured guidance during optimization. We propose a navel patch-wise-based diffusion model, which introduces the low curvature reverse trajectory to ensure stable parameter optimization and uncertainty guidance in the diffusion training process. Specifically, we randomly select patches of varying sizes from the entire image and apply patch-wise optimization between the generated image and the ground truth to enforce a stable optimization path in the diffusion model. Additionally, within each patch-wise region, an uncertainty network estimates the uncertainty, which is then integrated as a weighting factor in the diffusion process to balance areas of abrupt change in the image. Experimental evaluations on various datasets demonstrate that our method achieves significant improvements, particularly in experiments with real-world images. These results indicate that the proposed patch-wise-based diffusion model enhancements are effective. |
| format | Article |
| id | doaj-art-83f273fdab62405ab0055c5df55d17a3 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-83f273fdab62405ab0055c5df55d17a32025-08-20T02:48:06ZengMDPI AGApplied Sciences2076-34172025-02-01153160410.3390/app15031604Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image EnhancementLi Li0Jishen Peng1Yingcai Wan2Faculty of Electrical and Control, Liaoning Technical University, Huludao 125000, ChinaFaculty of Electrical and Control, Liaoning Technical University, Huludao 125000, ChinaSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaLow-light image enhancement is crucial for accurately interpreting images captured under low-lighting conditions. Existing low-light enhancement methods based on diffusion models have demonstrated effectiveness; however, they suffer from slow training processes and less structured guidance during optimization. We propose a navel patch-wise-based diffusion model, which introduces the low curvature reverse trajectory to ensure stable parameter optimization and uncertainty guidance in the diffusion training process. Specifically, we randomly select patches of varying sizes from the entire image and apply patch-wise optimization between the generated image and the ground truth to enforce a stable optimization path in the diffusion model. Additionally, within each patch-wise region, an uncertainty network estimates the uncertainty, which is then integrated as a weighting factor in the diffusion process to balance areas of abrupt change in the image. Experimental evaluations on various datasets demonstrate that our method achieves significant improvements, particularly in experiments with real-world images. These results indicate that the proposed patch-wise-based diffusion model enhancements are effective.https://www.mdpi.com/2076-3417/15/3/1604diffusion modellow lightuncertainty guidancepatch-wise |
| spellingShingle | Li Li Jishen Peng Yingcai Wan Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement Applied Sciences diffusion model low light uncertainty guidance patch-wise |
| title | Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement |
| title_full | Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement |
| title_fullStr | Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement |
| title_full_unstemmed | Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement |
| title_short | Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement |
| title_sort | patch wise based diffusion model with uncertainty guidance for low light image enhancement |
| topic | diffusion model low light uncertainty guidance patch-wise |
| url | https://www.mdpi.com/2076-3417/15/3/1604 |
| work_keys_str_mv | AT lili patchwisebaseddiffusionmodelwithuncertaintyguidanceforlowlightimageenhancement AT jishenpeng patchwisebaseddiffusionmodelwithuncertaintyguidanceforlowlightimageenhancement AT yingcaiwan patchwisebaseddiffusionmodelwithuncertaintyguidanceforlowlightimageenhancement |