Fully-automatic defects classification and restoration for STM images

The Scanning tunneling microscope (STM) is a micro instrument designed for surface morphology with nanometer precision. The restoration of the STM image defects usually needs human judgements and manual positioning because of the diversity of the morphology and the randomness of the defects. This paper provides a new fully-automatic method that combines deep convolutional neural classification network and unique restoration algorithms corresponding to different defects. Aimed at automatically processing compound defects in STM images, the method first predicts what kinds of defects a raw STM image has by a series of parallel binary classification networks, and then decides the process order according to the predicted labels, and finally restores the defects by corresponding global restoration algorithms in order. Experiment results prove the provided method can restore the STM images by self-judging, self-positioning, self-processing without any manual intervention.