Abstract: Image inpainting is an interpolation problem where an image with missing or damaged parts is restored. The most often used image inpainting applications are for pictures or films known or damaged partially. Discarding some unwanted parts, text or objects from the whole image space, special effects can be carried out using image restoration.
Complex mathematical models based on partial differential equations (PDE) or variational computing were proposed as techniques for restoring damaged or partially known images. Those methods are computational expensive and difficult to implement, even when a large serial processing computing power is available.
The Cellular Neural Networks (CNN) based parallel processing ensures computing-time reduction if the processing algorithm can be implemented on a continuous-time analogue CNN-UM (Cellular Neural/Nonlinear Networks Universal Machine) or using FPGA implemented emulated digital CNN-UM. Even if variational computing methods are used, the design of CNN templates ensuring the desired processing of the gray-scale image remains an important step.
In the present paper, some variational based CNN methods are presented and analyzed that can be used for the reconstruction of damaged or partially known images. Efficiency of these impanting methods can be enhanced by combining them with nonlinear template that ensures the growth of the local properties spreading area along with regional ones.

Brief Biography of the Speaker:
Alexandru Gacsadi received the M.Sc. and the Ph.D. degree in Electronic Engineering and Telecommunications, both from the “Politechnica” University of Timisoara, Romania, in 1986 and 2001, respectively. Since 1991 he is with the University of Oradea, Romania, and currently he is a professor at the Electronics Department of the Electrical Engineering and Information Technology Faculty responsible for teaching data acquisitions, cellular neural networks applications and robotics.
His research interests are in the area of neural networks, cellular neural networks and its applications, image processing and analysis with applications in medical imaging, processing and analysis of biomedical data, smart transducers, and robotics.
He has published more than 70 papers in national and international journals, conferences, workshops and symposium proceedings, authored 3 books and 5 application guides. He conducted or acting as active member for more than 15 research and development projects, grants and contracts in his field of interest.
Professor Alexandru Gacsadi has been involved in setting up national and international conferences as a reviewer and/or member of organizing committee. He is a member of the: IEEE Society (CAS), Society of Electronic Engineers from Romania and Romanian Society for Industrial Robotics.