Title :
Locally Gaussian exemplar based texture synthesis
Author :
Raad, Lara ; Desolneux, Agnes ; Morel, Jean-Michel
Author_Institution :
CMLA, Ecole Normale Super. de Cachan, Cachan, France
Abstract :
The main approaches to texture modeling are the statistical psychophysically inspired model and the patch-based model. In the first model the texture is characterized by a sophisticated statistical signature. The associated sampling algorithm estimates this signature from the example and produces a genuinely different texture. This texture nevertheless often loses accuracy. The second model boils down to a clever copy-paste procedure, which stitches verbatim copies of large regions of the example. We propose in this communication to involve a locally Gaussian texture model in the patch space. It permits to synthesize textures that are everywhere different from the original but with better quality than the purely statistical methods.
Keywords :
image texture; statistical analysis; clever copy-paste procedure; locally Gaussian exemplar based texture synthesis; patch-based model; sampling algorithm; statistical psychophysically inspired model; Biological system modeling; Gaussian distribution; Image denoising; Image edge detection; Visualization; Gaussian Modeling; Image Patches; Texture Synthesis;
Conference_Titel :
Image Processing (ICIP), 2014 IEEE International Conference on
Conference_Location :
Paris
DOI :
10.1109/ICIP.2014.7025946