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Adaptive real-time image smoothing using local binary patterns and Gaussian filters

: Teutsch, M.; Trantelle, Patrick; Beyerer, Jürgen

Postprint urn:nbn:de:0011-n-2869098 (3.5 MByte PDF)
MD5 Fingerprint: 6cca6177ac69fae480a4734d73a95589
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Created on: 17.4.2014

IEEE Signal Processing Society; Institute of Electrical and Electronics Engineers -IEEE-:
20th IEEE International Conference on Image Processing, ICIP 2013. Proceedings : 15-18 September 2013, Melbourne, Australia
Piscataway, NJ: IEEE, 2013
ISBN: 978-1-4799-2341-0
International Conference on Image Processing (ICIP) <20, 2013, Melbourne>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()
image denoising; image enhancement; locally adaptive; variable kernel size; texture analysis; LBPs

Image smoothing is widely used for enhancing the quality of single images or videos. There is a large amount of application areas such as machine vision, entertainment industry with smart TVs or consumer cameras, or surveillance and reconnaissance with different imaging sensors. In many cases it is not easy to find the trade-off between high smoothing quality and fast processing time. However, this is necessary for the mentioned applications as they are dependent on realtime computing. In this paper, we aim to find a good trade-off.
Local texture is analyzed with Local Binary Patterns (LBPs) which are used to adapt the size of a Gaussian smoothing kernel for each pixel. Real-time requirements are met by the implementation on a Graphical Processing Unit (GPU). An image of 512 x 512 pixels is processed in 2.6 ms.