Medical image alignment is an important tool for tracking patient conditions, but the quality of alignment is influenced bythe effectiveness of low-dose Cone-beam CT (CBCT) imaging and patient characteristics. To address these two issues, wepropose an unsupervised alignment method that incorporates a preprocessing super-resolution process. We constructed themodel based on a private clinical dataset and validated the enhancement of the super-resolution on alignment using clinicaland public data. Through all three experiments, we demonstrate that higher resolution data yields better results in the alignmentprocess. To fully constrain similarity and structure, a new loss function is proposed; Pearson correlation coefficientcombined with regional mutual information. In all test samples, the newly proposed loss function obtains higher resultsthan the common loss function and improve alignment accuracy. Subsequent experiments verified that, combined with thenewly proposed loss function, the super-resolution processed data boosts alignment, can reaching up to 9.58%. Moreover,this boost is not limited to a single model, but is effective in different alignment models. These experiments demonstratethat the unsupervised alignment method with super-resolution preprocessing proposed in this study effectively improvedalignment and plays an important role in tracking different patient conditions over time.