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.

翻译：无线胶囊内镜（WCE）是一种无痛、非侵入性的胃肠道疾病诊断工具。然而，受胃肠道解剖结构限制及硬件制造工艺约束，WCE视觉信号可能存在光照不足问题，导致筛查与检查过程复杂化。基于深度学习的医学低光照图像增强（LLIE）技术逐渐引起研究者关注。鉴于去噪扩散概率模型（DDPM）在计算机视觉领域的蓬勃发展，本文提出一种融合多尺度卷积神经网络（CNN）与逆扩散过程的WCE低光照图像增强框架。多尺度设计使模型能够同时保留高分辨率表征与低尺度上下文信息，同时提出弯曲小波注意力（CWA）模块用于高频特征与局部特征学习。此外，我们结合逆扩散过程进一步优化浅层输出，生成最逼真的图像。将所提方法与十种当前最优（SOTA）LLIE方法进行对比，在定量与定性分析中均表现显著优势。在胃肠道疾病分割任务中的优异性能进一步验证了本模型的临床潜力。我们的代码已公开。