Inkjet Defect Detection Method based on Printing Characterization

With the advancement of digital printing technology, the rapid detection of inkjet defects have become critical research areas in OnePass inkjet printing. These defects can disrupt the continuity and smoothness of the image, leading to a decrease in print quality. To overcome these issues, this study proposed an inkjet defect detection method based on printing characterization. Image processing technology was used to obtain the printing characterization information of the printer and extract the information of ink point positions. Optimizing the allocation matrix through Sinkhorn algorithm and combining it with Robust Point Matching algorithm to construct the transmission objective function was accomplished to obtain the optimal point set matching model. This model serves two purposes: diagnosing the nozzle function status of each printhead and quantifying the alignment errors between printheads. Experiments demonstrated the high precision of this detection method. We analyzed the impact of related parameters on the model’s performance and assessed changes in image quality under different alignment errors. This approach provides a new solution for optimizing printer maintenance.