Unified-EGformer: Exposure Guided Lightweight Transformer for Mixed-Exposure Image Enhancement
<|reference_start|>Unified-EGformer: Exposure Guided Lightweight Transformer for Mixed-Exposure Image Enhancement: Despite recent strides made by AI in image processing, the issue of mixed exposure, pivotal in many real-world scenarios like surveillance and photography, remains inadequately addressed. Traditional image enhancement techniques and current transformer models are limited with primary focus on either overexposure or underexposure. To bridge this gap, we introduce the Unified-Exposure Guided Transformer (Unified-EGformer). Our proposed solution is built upon advanced transformer architectures, equipped with local pixel-level refinement and global refinement blocks for color correction and image-wide adjustments. We employ a guided attention mechanism to precisely identify exposure-compromised regions, ensuring its adaptability across various real-world conditions. U-EGformer, with a lightweight design featuring a memory footprint (peak memory) of only $\sim$1134 MB (0.1 Million parameters) and an inference time of 95 ms (9.61x faster than the average), is a viable choice for real-time applications such as surveillance and autonomous navigation. Additionally, our model is highly generalizable, requiring minimal fine-tuning to handle multiple tasks and datasets with a single architecture.<|reference_end|>
@article{adhikarla2024unified-egformer:,
    title={Unified-EGformer: Exposure Guided Lightweight Transformer for
  Mixed-Exposure Image Enhancement},
    author={Eashan Adhikarla, Kai Zhang, Rosaura G. VidalMata, Manjushree Aithal,
  Nikhil Ambha Madhusudhana, John Nicholson, Lichao Sun, Brian D. Davison},
    journal={arXiv preprint arXiv:2407.13170},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13170},
    primaryClass={cs.CV cs.AI}
}