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

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.