Adaptive Image Processing Algorithms for Printing (eBook)

This book presents essential algorithms for the image processing pipeline of photo-printers and accompanying software tools, offering an exposition of multiple image enhancement algorithms, smart aspect-ratio changing techniques for borderless printing and approaches for non-standard printing modes. All the techniques described are content-adaptive and operate in an automatic mode thanks to machine learning reasoning or ingenious heuristics. The first part includes algorithms, for example, red-eye correction and compression artefacts reduction, that can be applied in any photo processing application, while the second part focuses specifically on printing devices, e.g. eco-friendly and anaglyph printing. The majority of the techniques presented have a low computational complexity because they were initially designed for integration in system-on-chip. The book reflects the authors' practical experience in algorithm development for industrial R&D.

Ilia V. Safonov graduated from Moscow Engineering Physics Institute (at present time National Research Nuclear University MEPhI) in 1993 as engineer-physicist. He obtained PhD degree in computer science in 1997. Since 2000, he is associated professor in the department of Computer Science and Control Systems at MEPhI. At last decade, he had senior researcher position in RnD of Samsung, Nokia and Intel. At present time, Dr. Ilia Safonov is principal research-scientist at Schlumberger Moscow Research. His interests include image and signal processing, machine learning, measurement systems, computer graphics and vision.

Ilya V. Kurilin received his MS degree in radio engineering from Novosibirsk State Technical University (NSTU), Russia in 1999 and his PhD degree in theoretical bases of informatics from NSTU in 2006. In 2007, Dr. I. Kurilin joined Image Processing Group, Samsung R&D Institute in Moscow, Russia, where he is engaged in photo and document image processing projects. Since 2015, he leads Video and Image Processing Laboratory specialized in real-time semantic processing of visual data for mobile devices.

Michael N. Rychagov received MS degree in acoustical imaging and PhD degree from the Moscow State University (MSU) in 1986 and 1989, respectively. In 2000, he received a Dr.Sc. degree (Habilitation) from the same University. From 1991, he is involved in teaching and research at the Moscow Institute of Electronic Technology (MIET) as an associate professor in the Department of Theoretical and Experimental Physics (1998), professor in the Department of Biomedical Systems (2008), professor in the Department of Informatics and SW for Computer Systems (2014). Since 2004, he joined Samsung R&D Institute in Moscow, Russia (SRR) working on imaging algorithms for printing, scanning and copying,  TV and display technologies, multimedia and tomographic areas. Currently, he is Director of the Advanced Mobile Solution Division at SRR. His technical and scientific interests are image and video signal processing, biomedical visualization, engineering applications of machine learning and artificial intelligence.

Ekaterina V. Tolstaya received her MS degree in applied mathematics from Moscow State University, in 2000. In 2004, she completed her MS degree in geophysics from University of Utah, USA, where she worked on inverse scattering in electromagnetics. Since 2004, she worked on problems of image processing and reconstruction in Samsung R&D Institute in Moscow, Russia. Based on these investigations she obtained in 2011 her PhD degree with research on image processing algorithms for printing. In 2014, she continued her career with Align Technology on problems involving computer vision, 3D geometry and machine learning.

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This book presents essential algorithms for the image processing pipeline of photo-printers and accompanying software tools, offering an exposition of multiple image enhancement algorithms, smart aspect-ratio changing techniques for borderless printing and approaches for non-standard printing modes. All the techniques described are content-adaptive and operate in an automatic mode thanks to machine learning reasoning or ingenious heuristics. The first part includes algorithms, for example, red-eye correction and compression artefacts reduction, that can be applied in any photo processing application, while the second part focuses specifically on printing devices, e.g. eco-friendly and anaglyph printing. The majority of the techniques presented have a low computational complexity because they were initially designed for integration in system-on-chip. The book reflects the authors' practical experience in algorithm development for industrial R&D.