Abstract
The present paper focuses on sharpness enhancement and noise removal in two dimensional gray scale images. In the grid smoothing approach, the image is represented by a graph in which the nodes represent the pixels and the edges reflect the connectivity. A cost function is defined using the spatial coordinates of the nodes and the gray levels present in the image. The minimisation of the cost function leads to new spatial coordinates for each node. Using an adequate cost function, the grid is compressed in the regions with large gradient values and relaxed in the other regions. The result is a grid which fits accurately the objects in the image. In the presented framework, the noise in the initial image is removed using a mesh smoothing approach. The edges are then enhanced using the grid smoothing. If the level of noise is low, the grid smoothing is applied directly to the image. The mathematical framework of the method is introduced in the paper. The processing chain is tested on natural images.
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Noel, G., Djouani, K., Hamam, Y. (2010). Grid Smoothing for Image Enhancement. In: Kim, Th., Lee, Yh., Kang, BH., Ślęzak, D. (eds) Future Generation Information Technology. FGIT 2010. Lecture Notes in Computer Science, vol 6485. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17569-5_14
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DOI: https://doi.org/10.1007/978-3-642-17569-5_14
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