Simulating and Fixing Visible Anomalies in Thermal Inkjet Printing with Machine Learning

HP Inc. Computer Science, 2018-19

Liaison(s): Steve Bauer, Perry Lieber, Aaron Rosen ’16
Advisor(s): Lucas Bang
Students(s): Willis Sanchez-duPont, Collin Valleroy, Olivia Watkins (PM), Gavin Yancey, Jasmine Zhu

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.