EndoUIC: Promptable Diffusion Transformer for Unified Illumination Correction in Capsule Endoscopy
<|reference_start|>EndoUIC: Promptable Diffusion Transformer for Unified Illumination Correction in Capsule Endoscopy: Wireless Capsule Endoscopy (WCE) is highly valued for its non-invasive and painless approach, though its effectiveness is compromised by uneven illumination from hardware constraints and complex internal dynamics, leading to overexposed or underexposed images. While researchers have discussed the challenges of low-light enhancement in WCE, the issue of correcting for different exposure levels remains underexplored. To tackle this, we introduce EndoUIC, a WCE unified illumination correction solution using an end-to-end promptable diffusion transformer (DiT) model. In our work, the illumination prompt module shall navigate the model to adapt to different exposure levels and perform targeted image enhancement, in which the Adaptive Prompt Integration (API) and Global Prompt Scanner (GPS) modules shall further boost the concurrent representation learning between the prompt parameters and features. Besides, the U-shaped restoration DiT model shall capture the long-range dependencies and contextual information for unified illumination restoration. Moreover, we present a novel Capsule-endoscopy Exposure Correction (CEC) dataset, including ground-truth and corrupted image pairs annotated by expert photographers. Extensive experiments against a variety of state-of-the-art (SOTA) methods on four datasets showcase the effectiveness of our proposed method and components in WCE illumination restoration, and the additional downstream experiments further demonstrate its utility for clinical diagnosis and surgical assistance.<|reference_end|>
@article{bai2024endouic:,
    title={EndoUIC: Promptable Diffusion Transformer for Unified Illumination
  Correction in Capsule Endoscopy},
    author={Long Bai, Tong Chen, Qiaozhi Tan, Wan Jun Nah, Yanheng Li, Zhicheng
  He, Sishen Yuan, Zhen Chen, Jinlin Wu, Mobarakol Islam, Zhen Li, Hongbin Liu,
  Hongliang Ren},
    journal={arXiv preprint arXiv:2406.13705},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13705},
    primaryClass={eess.IV cs.AI cs.CV}
}