Through use of neural network architectures, our project aims to correct for common visible static and dynamic printing defects. Our architecture creates a simulated model of the printer hardware based on a database of printed and scanned images. The model then learns to remove future print defects via “anti-defect” patterns inserted in the image that counteract the different types of observed defects.