Progressive enhancement and restoration for mural images under low-light and defected conditions based on multi-receptive field strategy
<|reference_start|>Progressive enhancement and restoration for mural images under low-light and defected conditions based on multi-receptive field strategy: Ancient murals are valuable cultural heritage with great archaeological value. They provide insights into ancient religions, ceremonies, folklore, among other things through their content. However, due to long-term oxidation and inadequate protection, ancient murals have suffered continuous damage, including peeling and mold etc. Additionally, since ancient murals were typically painted indoors, the light intensity in images captured by digital devices is often low. The poor visibility hampers the further restoration of damaged areas. To address the escalating damage to ancient murals and facilitate batch restoration at archaeological sites, we propose a two-stage restoration model with automatic defect area detection strategy which called MER(Mural Enhancement and Restoration net) for ancient murals that are damaged and have been captured in low light. Our two-stage model not only enhances the visual quality of restored images but also achieves commendable results in relevant metric evaluations compared with other competitors. Furthermore, we have launched a website dedicated to the restoration of ancient mural paintings, utilizing the proposed model. Code is available at https://gitee.com/bbfan2024/MER.git.<|reference_end|>
@article{wei2024progressive,
    title={Progressive enhancement and restoration for mural images under low-light
  and defected conditions based on multi-receptive field strategy},
    author={Xiameng Wei, Binbin Fan, Ying Wang, Yanxiang Feng and Laiyi Fu},
    journal={arXiv preprint arXiv:2405.08245},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.08245},
    primaryClass={cs.CV cs.AI}
}