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- W2396199369 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." @default.
- W2396199369 created "2016-06-24" @default.
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- W2396199369 date "2010-06-13" @default.
- W2396199369 modified "2023-09-26" @default.
- W2396199369 title "Plenary lecture 1: variational based image inpainting methods by using cellular neural networks" @default.
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