EndoUIC: Promptable Diffusion Transformer for Unified Illumination Correction in Capsule Endoscopy
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910517377892352 |
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| author | Bai, Long Chen, Tong Tan, Qiaozhi Nah, Wan Jun Li, Yanheng He, Zhicheng Yuan, Sishen Chen, Zhen Wu, Jinlin Islam, Mobarakol Li, Zhen Liu, Hongbin Ren, Hongliang |
| author_facet | Bai, Long Chen, Tong Tan, Qiaozhi Nah, Wan Jun Li, Yanheng He, Zhicheng Yuan, Sishen Chen, Zhen Wu, Jinlin Islam, Mobarakol Li, Zhen Liu, Hongbin Ren, Hongliang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_13705 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EndoUIC: Promptable Diffusion Transformer for Unified Illumination Correction in Capsule Endoscopy Bai, Long Chen, Tong Tan, Qiaozhi Nah, Wan Jun Li, Yanheng He, Zhicheng Yuan, Sishen Chen, Zhen Wu, Jinlin Islam, Mobarakol Li, Zhen Liu, Hongbin Ren, Hongliang Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | EndoUIC: Promptable Diffusion Transformer for Unified Illumination Correction in Capsule Endoscopy |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.13705 |