  Significance: Endoscopic screening for esophageal cancer may enable early
cancer diagnosis and treatment. While optical microendoscopic technology has
shown promise in improving specificity, the limited field of view (<1 mm)
significantly reduces the ability to survey large areas efficiently in
esophageal cancer screening. Aim: To improve the efficiency of endoscopic
screening, we proposed a novel end-expandable endoscopic optical fiber probe
for larger field of visualization and employed a deep learning-based image
super-resolution (DL-SR) method to overcome the issue of limited sampling
capability. Approach: To demonstrate feasibility of the end-expandable optical
fiber probe, DL-SR was applied on simulated low-resolution (LR) microendoscopic
images to generate super-resolved (SR) ones. Varying the degradation model of
image data acquisition, we identified the optimal parameters for optical fiber
probe prototyping. The proposed screening method was validated with a human
pathology reading study. Results: For various degradation parameters
considered, the DL-SR method demonstrated different levels of improvement of
traditional measures of image quality. The endoscopist interpretations of the
SR images were comparable to those performed on the high-resolution ones.
Conclusions: This work suggests avenues for development of DL-SR-enabled
end-expandable optical fiber probes to improve high-yield esophageal cancer
screening.

Image and Video Processing