Although wireless capsule endoscopy is the preferred modality for diagnosis
and assessment of small bowel diseases, the poor camera resolution is a
substantial limitation for both subjective and automated diagnostics.
Enhanced-resolution endoscopy has shown to improve adenoma detection rate for
conventional endoscopy and is likely to do the same for capsule endoscopy. In
this work, we propose and quantitatively validate a novel framework to learn a
mapping from low-to-high resolution endoscopic images. We combine conditional
adversarial networks with a spatial attention block to improve the resolution
by up to factors of 8x, 10x, 12x, respectively. Quantitative and qualitative
studies performed demonstrate the superiority of EndoL2H over state-of-the-art
deep super-resolution methods DBPN, RCAN and SRGAN. MOS tests performed by 30
gastroenterologists qualitatively assess and confirm the clinical relevance of
the approach. EndoL2H is generally applicable to any endoscopic capsule system
and has the potential to improve diagnosis and better harness computational
approaches for polyp detection and characterization. Our code and trained
models are available at this https URL

本研究提出并定量验证了从低分辨率到高分辨率内窥镜图像的映射的新框架，并结合条件对抗网络和空间注意力块将分辨率提高了 8 倍至 12 倍，该方法被 30 名胃肠病专家定性评估并确认具有临床重要性，其具有潜力提高诊断精度并更好地利用计算方法进行息肉检测和表征。