MESS: Multi-Exposure Sequence Synthesis for Generalizable Image Enhancement
To address this issue, we propose a novel framework to synthesize realistic training pairs for generalizable image enhancement in the wild.
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Key points
- 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.
- Specifically, we first derive multi-exposure sequences (MES) from high-bit RAW images by rendering them under different exposure settings through an emulated ISP pipeline.
- Using these RAW-derived MES as supervision, we train a diffusion-based RGB-to-MES generator to synthesize plausible exposure sequences from a single 8-bit RGB image, extending the synthesis pipeline from limited RAW collections to large-scale in-the-wild RGB data.
- With the proposed MES Synthesis (MESS) approach, we construct a dataset of 500K training pairs and train a lightweight network, which demonstrates significantly better generalization performance than existing models across various image enhancement tasks.
Sources (1)
- [1]MESS: Multi-Exposure Sequence Synthesis for Generalizable Image EnhancementarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 01:19 PM
To address this issue, we propose a novel framework to synthesize realistic training pairs for generalizable image enhancement in the wild.
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.
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