Abstract
Abstract
We introduce an advanced feature-correlation approach for evaluating the accuracy of data completion in scanning probe microscopy (SPM). Our method utilizes characteristic patterns from conventional SPM images and their reconstructions via data interpolation. We develop a refined comparative evaluation algorithm based on correlation coefficients. This algorithm provides a precise assessment by effectively addressing SPM-specific distortions such as thermal drift, feedback error, and noise limitations often overlooked by traditional metrics such as peak signal-to-noise ratio and structural similarity index measure. The effectiveness of our approach is demonstrated through its application in high-resolution and extensive scanning tunneling microscopy assessments.