  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.
Furthermore, we combine the reverse diffusion procedure to further optimize the
shallow output and generate the most realistic image. The proposed method is
compared with ten 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.
