Abstract
Capsule endoscopy has enabled minimally invasive gastrointestinal imaging, but its clinical utility is limited by the inherently low resolution of captured images due to hardware, power, and transmission constraints. This limitation hampers the identification of fine-grained mucosal textures and subtle pathological features essential for early diagnosis. This work investigates a diffusion-based super-resolution framework to enhance capsule endoscopy images in a data-driven and anatomically consistent manner. We adopt the SR3 (Super-Resolution via Repeated Refinement) framework built upon Denoising Diffusion Probabilistic Models (DDPMs) to learn a probabilistic mapping from low-resolution to high-resolution images. Unlike GAN-based approaches that often suffer from training instability and hallucination artifacts, diffusion models provide stable likelihood-based training and improved structural fidelity. The HyperKvasir dataset, a large-scale publicly available gastrointestinal endoscopy dataset, is used for training and evaluation. Quantitative results demonstrate that the proposed method significantly outperforms bicubic interpolation and GAN-based super-resolution methods such as ESRGAN, achieving PSNR of 27.5 dB and SSIM of 0.65 for a baseline model, and improving to 29.3 dB and 0.71 with architectural enhancements including attention mechanisms. Qualitative results show improved preservation of anatomical boundaries, vascular patterns, and lesion structures. These findings indicate that diffusion-based super-resolution is a promising approach for enhancing non-invasive medical imaging, particularly in capsule endoscopy where image resolution is fundamentally constrained.
Abstract (translated)
胶囊内镜技术实现了微创胃肠成像，但其临床应用受限于图像固有的低分辨率，这主要是由于硬件、电源和传输的限制。这种局限性阻碍了对细微粘膜纹理及早期诊断所需的小病变特征的识别。本研究探讨了一种基于扩散的超分辨率框架，以数据驱动且解剖学一致的方式增强胶囊内镜图像。我们采用了SR3（通过重复细化实现的超分辨率）框架，并在其基础上构建了去噪扩散概率模型（DDPMs），以此来学习从低分辨率到高分辨率图像的概率映射。与常因训练不稳定和幻觉伪影问题而受困的GAN（生成对抗网络）方法不同，扩散模型提供了基于似然性的稳定训练以及结构保真度的提升。 为了训练和评估，我们使用了HyperKvasir数据集——一个大规模公开可用的胃肠内镜图像数据集。定量结果显示所提出的方法在基准模型上达到了27.5 dB的PSNR（峰值信噪比）和0.65的SSIM（结构相似性指数），显著优于双三次插值及GAN超分辨率方法如ESRGAN；通过加入注意力机制等架构改进，性能进一步提升至29.3 dB PSNR和0.71 SSIM。定性结果表明该模型能更好地保留解剖边界、血管模式以及病变结构。 这些发现表明基于扩散的超分辨率技术是增强非侵入性医学成像的一个有前景的方法，特别是在图像分辨率固有限制的胶囊内镜检查中尤为显著。
URL
https://arxiv.org/abs/2512.22209
