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.

翻译：无线胶囊内窥镜（WCE）因其无创、无痛的特性而备受重视，但其成像效果常受硬件限制和复杂内部动态导致的照明不均影响，易产生过曝或欠曝图像。尽管已有研究探讨WCE中的低照度增强挑战，针对不同曝光水平的校正问题仍未得到充分探索。为此，我们提出EndoUIC——一种基于端到端可提示扩散Transformer（DiT）模型的WCE统一光照校正方案。在本工作中，光照提示模块引导模型适应不同曝光水平并执行针对性图像增强，其中自适应提示集成（API）模块与全局提示扫描器（GPS）模块进一步促进了提示参数与特征间的协同表征学习。此外，U形恢复DiT模型能够捕获长程依赖关系与上下文信息，实现统一的光照恢复。我们还构建了新颖的胶囊内窥镜曝光校正（CEC）数据集，包含由专业摄影师标注的真实图像与退化图像对。在四个数据集上针对多种前沿方法的广泛实验表明，我们提出的方法及各组件在WCE光照恢复中具有显著效果，下游补充实验进一步验证了其在临床诊断与手术辅助中的应用价值。