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 $8\times $ , $10\times $ , $12\times $ , respectively. Quantitative and qualitative studies demonstrate the superiority of EndoL2H over state-of-the-art deep super-resolution methods Deep Back-Projection Networks (DBPN), Deep Residual Channel Attention Networks (RCAN) and Super Resolution Generative Adversarial Network (SRGAN). Mean Opinion Score (MOS) tests were 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 https://github.com/CapsuleEndoscope/EndoL2H .

中文翻译：

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EndoL2H：胶囊内窥镜的深度超高分辨率。

尽管无线胶囊内窥镜检查是诊断和评估小肠疾病的首选方式，但较差的相机分辨率对于主观诊断和自动诊断都是一个很大的限制。高分辨率内窥镜已显示出可提高常规内窥镜对腺瘤的检出率，并且对于胶囊内窥镜也可能如此。在这项工作中，我们提出并定量验证了一种新颖的框架，以学习从低到高分辨率内窥镜图像的映射。我们将条件对抗网络与空间关注区域相结合，以将分辨率提高多达 $ 8 \次$ ， $ 10 \次$ ， $ 12 \次$ ， 分别。定量和定性研究表明，EndoL2H优于最新的深层超分辨率方法，深层背投影网络（DBPN），深层残留通道关注网络（RCAN）和超分辨率生成对抗网络（SRGAN）。由30位肠胃病学医师进行的平均意见评分（MOS）测试定性评估并确认该方法的临床相关性。EndoL2H通常适用于任何内窥镜胶囊系统，并具有改善诊断和更好地利用息肉检测和表征的计算方法的潜力。我们的代码和训练有素的模型可在以下位置获得https://github.com/CapsuleEndoscope/EndoL2H 。