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Wireless capsule endoscopy (WCE) is a painless and non-invasive diagnostictool for gastrointestinal (GI) diseases. However, due to GI anatomicalconstraints and hardware manufacturing limitations, WCE vision signals maysuffer from insufficient illumination, leading to a complicated screening andexamination procedure. Deep learning-based low-light image enhancement (LLIE)in the medical field gradually attracts researchers. Given the exuberantdevelopment of the denoising diffusion probabilistic model (DDPM) in computervision, we introduce a WCE LLIE framework based on the multi-scaleconvolutional neural network (CNN) and reverse diffusion process. Themulti-scale design allows models to preserve high-resolution representation andcontext information from low-resolution, while the curved wavelet attention(CWA) block is proposed for high-frequency and local feature learning.Furthermore, we combine the reverse diffusion procedure to further optimize theshallow output and generate the most realistic image. The proposed method iscompared with ten state-of-the-art (SOTA) LLIE methods and significantlyoutperforms quantitatively and qualitatively. The superior performance on GIdisease segmentation further demonstrates the clinical potential of ourproposed model. Our code is publicly accessible.

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