Unsupervised Low-light Image Enhancement with Lookup Tables and Diffusion Priors
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866929518491467776 |
|---|---|
| author | Lin, Yunlong Fu, Zhenqi Wen, Kairun Ye, Tian Chen, Sixiang Meng, Ge Wang, Yingying Huang, Yue Tu, Xiaotong Ding, Xinghao |
| author_facet | Lin, Yunlong Fu, Zhenqi Wen, Kairun Ye, Tian Chen, Sixiang Meng, Ge Wang, Yingying Huang, Yue Tu, Xiaotong Ding, Xinghao |
| contents | Low-light image enhancement (LIE) aims at precisely and efficiently recovering an image degraded in poor illumination environments. Recent advanced LIE techniques are using deep neural networks, which require lots of low-normal light image pairs, network parameters, and computational resources. As a result, their practicality is limited. In this work, we devise a novel unsupervised LIE framework based on diffusion priors and lookup tables (DPLUT) to achieve efficient low-light image recovery. The proposed approach comprises two critical components: a light adjustment lookup table (LLUT) and a noise suppression lookup table (NLUT). LLUT is optimized with a set of unsupervised losses. It aims at predicting pixel-wise curve parameters for the dynamic range adjustment of a specific image. NLUT is designed to remove the amplified noise after the light brightens. As diffusion models are sensitive to noise, diffusion priors are introduced to achieve high-performance noise suppression. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods in terms of visual quality and efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18899 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unsupervised Low-light Image Enhancement with Lookup Tables and Diffusion Priors Lin, Yunlong Fu, Zhenqi Wen, Kairun Ye, Tian Chen, Sixiang Meng, Ge Wang, Yingying Huang, Yue Tu, Xiaotong Ding, Xinghao Computer Vision and Pattern Recognition Image and Video Processing Low-light image enhancement (LIE) aims at precisely and efficiently recovering an image degraded in poor illumination environments. Recent advanced LIE techniques are using deep neural networks, which require lots of low-normal light image pairs, network parameters, and computational resources. As a result, their practicality is limited. In this work, we devise a novel unsupervised LIE framework based on diffusion priors and lookup tables (DPLUT) to achieve efficient low-light image recovery. The proposed approach comprises two critical components: a light adjustment lookup table (LLUT) and a noise suppression lookup table (NLUT). LLUT is optimized with a set of unsupervised losses. It aims at predicting pixel-wise curve parameters for the dynamic range adjustment of a specific image. NLUT is designed to remove the amplified noise after the light brightens. As diffusion models are sensitive to noise, diffusion priors are introduced to achieve high-performance noise suppression. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods in terms of visual quality and efficiency. |
| title | Unsupervised Low-light Image Enhancement with Lookup Tables and Diffusion Priors |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2409.18899 |