Little Strokes Fell Great Oaks: Boosting the Hierarchical Features for Multi-exposure Image Fusion
<|reference_start|>Little Strokes Fell Great Oaks: Boosting the Hierarchical Features for Multi-exposure Image Fusion: In recent years, deep learning networks have made remarkable strides in the domain of multi-exposure image fusion. Nonetheless, prevailing approaches often involve directly feeding over-exposed and under-exposed images into the network, which leads to the under-utilization of inherent information present in the source images. Additionally, unsupervised techniques predominantly employ rudimentary weighted summation for color channel processing, culminating in an overall desaturated final image tone. To partially mitigate these issues, this study proposes a gamma correction module specifically designed to fully leverage latent information embedded within source images. Furthermore, a modified transformer block, embracing with self-attention mechanisms, is introduced to optimize the fusion process. Ultimately, a novel color enhancement algorithm is presented to augment image saturation while preserving intricate details. The source code is available at https://github.com/ZhiyingDu/BHFMEF.<|reference_end|>
@article{mu2024little,
    title={Little Strokes Fell Great Oaks: Boosting the Hierarchical Features for
  Multi-exposure Image Fusion},
    author={Pan Mu, Zhiying Du, Jinyuan Liu, Cong Bai},
    journal={Proceedings of the 31st ACM International Conference on
  Multimedia, October 2023, Pages 2985-2993},
    year={2024},
    doi={10.1145/3581783.3612561},
    archivePrefix={arXiv},
    eprint={2404.06033},
    primaryClass={cs.CV}
}