Narrow Band Imaging (NBI) is a relatively new endoscopic imaging modality that provides enhanced views of the blood vessels situated near the surface of the airway walls. Certain vessel patterns have been shown to be potential indications of cancerous lesions developing in the airways. Some recent e orts have strived to use NBI bronchoscopy to locate such vessel patterns. To find these patterns, the physician is forced to navigate the bronchoscope through the airways and manually observe potential mucosal (airway wall) vascular patterns. Unfortunately, the bronchoscopic video is often degraded by motion blur and other artifacts, thereby making an already tedious search more impractical. In addition, the degraded video can decrease the potential performance of automated vascular analysis methods. We propose to exploit the richness of the highly informative video sequence by exploring methods for super-resolution video reconstruction and image deblurring|methods which have seen spectacular success in other domains for obtaining high-resolution (HR) images from a sequence of low-resolution (LR) images. Through data derived from NBI video sequences of lung-cancer patients, we demonstrate that such methods can both substantially improve the image quality of images depicting NBI-based vascular patterns and also quantitatively enable more effective automatic segmentation of the vascular patterns.