PDEng on Deep learning to detect bitmap artefacts

Updated: 27 days ago
Deadline: 03 Oct 2021

Candidate

Canon Production printing develops high-end digital inkjet printer systems for the high-volume and graphic arts professional printing markets. In such printers' high-resolution bitmaps are prepared for printing using a fast image processing implementation that is able to match the demanding data rate requirements of the systems. This image processing ensures that the prints are delivered with the best possible print quality and without any artifacts.

To ensure that the image quality targets are met, the image processing implementations are rigorously tested, both using offline tooling and by printing extensive test sets on real printers. However, finding artifacts is a challenge due to the bulk of data, and sometimes feels like looking for a needle in a haystack: Visually inspecting the output bitmaps that contain hundreds of millions of pixels is very labor intensive, as is looking for artifacts in big stacks of paper.

Canon Production Printing is looking at ways to improve the identification of print artifacts in image processing output in order to further improve the quality of our deliveries. One of the planned improvement steps is the integration of machine learning algorithms during regression testing as a way to help the developers identify print artifacts in the image processing output before the software is released to be integrated into the print engine software.

This PDEng project will consist of deep learning algorithm development and recreation/
simulation of image quality artifacts. To obtain training data for the machine learning, existing (mostly artifact-free) regression test-sets can be used. In addition, miscellaneous types of artifacts can be (re-) created by building past versions of the software, as well as by manipulation of input data to the software. Additional artifacts may be simulated by adjusting the image processing output bitmaps. The training data will be used to create deep neural networks with the goal of flagging test cases where the output is expected to contain artifacts. For this application, a good network should combine high processing speed with good classification performance. The resulting network will be integrated into the build and test environment to run during (daily) regression testing and automated builds, where it should flag the bitmaps most likely to contain image artifacts. These bitmaps can then be inspected further manually.

Electronic Systems group at TU/e and Canon Production Printing

The Electronic Systems group consists of seven full professors, two associate professors, eight assistant professors, several postdocs, about 40 PDEng and PhD candidates and support staff. The ES group is world-renowned for its design automation and embedded systems research.
It is our ambition to provide a scientific basis for design trajectories of electronic systems, ranging from digital circuits to cyber-physical systems. The trajectories are constructive and lead to high quality, cost-effective systems with predictable properties (functionality, timing, reliability, power dissipation, and cost).

Canon is global leader in consumer and professional imaging. One of Canon's goals is to be the
#1 in printing. Our strategic imperative and incentive is to constantly look for opportunities to improve our organization, business, culture and brand and to proactively pursue our ambitions. Founded in 1877 in Venlo, the Netherlands, Canon Production Printing has a long history of technical innovation and development. A key asset is inkjet, a game-changing and widely applicable imaging technology. Our ambition is to build on our expertise in jetting for high-volume, high-speed printing and to position ourselves as a thought leader in jetting technology and applications. Jetting is key to our future, and we are energized by our exploration of its extensive possibilities.


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