MESS: Multi-Exposure Sequence Synthesis for Generalizable Image Enhancement
Despite the significant progress in image enhancement under adverse illuminations, such as low-light image enhancement, exposure correction, and backlit image enhancement, existing models show limited generalizability to different scenarios. A major bottleneck lies in the lack of large-scale and diverse training data, as these tasks typically rely on manually captured, precisely aligned image pairs that are expensive to scale up. Although synthetic data have been explored as an alternative, existing synthesis methods are limited in modeling complex adverse illumination conditions and camera imaging pipelines. To address this issue, we propose a novel framework to synthesize realistic trai...
| Authors | Ruodai Cui, Shuaizheng Liu, Rongyuan Wu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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