Problem
Research questions and friction points this paper is trying to address.
Wireless Capsule Endoscopy
Low-Resolution
Image Super-Resolution
UnCapsTSR: An Unsupervised Transformer-based Image Super-Resolution Approach for Capsule Endoscopy Images
🤖 AI Summary
本文提出UnCapsTSR，一种基于Transformer的无监督GAN框架，用于提高无线胶囊内镜图像的空间分辨率，无需真实低-高分辨率图像对，并引入新的评价指标EndoQM。
📝 Abstract
Wireless Capsule Endoscopy (WCE) captures and streams video while passing through a patient's Gastrointestinal (GI) tract and is used to examine its irregularities. Although advantageous over conventional endoscopy, WCE suffers from limitations related to capsule size and wireless transmission, resulting in images with coarser resolution. This work presents UnCapsTSR, an unsupervised transformer-based Generative Adversarial Network (GAN) framework for improving the spatial resolution of Low-Resolution (LR) WCE images. The proposed method accomplishes SR without explicit degradation estimation of real-world LR data and eliminates the need for true LR-HR pairs. UnCapsTSR employs a Bilateral Total Variation (BTV) loss to ensure spatial continuity in SR images. A newly curated dataset from the Kvasir Capsule dataset is also presented for training WCE SR models. Generalizability is validated on KID and GIANA datasets that are not used during training. A new non-reference metric, Endoscopy Quality Metric (EndoQM), is introduced for quantitative evaluation of domain-specific WCE data. Experiments demonstrate consistent improvement over state-of-the-art unsupervised SR approaches using NIQE, BRISQUE, PIQE, and EndoQM. Statistical evaluation shows 40 to 80 percent improvement in EndoQM from LR to SR across the evaluated datasets.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Unsupervised Transformer-based GAN
Bilateral Total Variation Loss
Endoscopy Quality Metric
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