  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.
