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LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion

Bai, L; Chen, T; Wu, Y; Wang, A; Islam, M; Ren, H; (2023) LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion. In: International Conference on Medical Image Computing and Computer-Assisted Intervention MICCAI 2023: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. (pp. pp. 34-44). Springer: Cham, Switzerland. Green open access

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Abstract

Wireless capsule endoscopy (WCE) is a painless and non-invasive diagnostic tool for gastrointestinal (GI) diseases. However, due to GI anatomical constraints and hardware manufacturing limitations, WCE vision signals may suffer from insufficient illumination, leading to a complicated screening and examination procedure. Deep learning-based low-light image enhancement (LLIE) in the medical field gradually attracts researchers. Given the exuberant development of the denoising diffusion probabilistic model (DDPM) in computer vision, we introduce a WCE LLIE framework based on the multi-scale convolutional neural network (CNN) and reverse diffusion process. The multi-scale design allows models to preserve high-resolution representation and context information from low-resolution, while the curved wavelet attention (CWA) block is proposed for high-frequency and local feature learning. Moreover, we combine the reverse diffusion procedure to optimize the shallow output further and generate images highly approximate to real ones. The proposed method is compared with eleven state-of-the-art (SOTA) LLIE methods and significantly outperforms quantitatively and qualitatively. The superior performance on GI disease segmentation further demonstrates the clinical potential of our proposed model. Our code is publicly accessible at github.com/longbai1006/LLCaps.

Type: Proceedings paper
Title: LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion
Event: MICCAI 2023: Medical Image Computing and Computer Assisted Intervention
ISBN-13: 9783031439988
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-031-43999-5_4
Publisher version: http://dx.doi.org/10.1007/978-3-031-43999-5_4
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery-pp.ucl.ac.uk/id/eprint/10184123
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