Abstract:
A lot of work has been done on texture generation techniques. Deep learning
based image generation techniques have been extremely successful in generating
realistic images. Moreover, reaction-di usion systems have also been successful
in generating a wide variety of textures. However, the reaction-di usion systems
have never been incorporated in modern deep learning architectures. On the other
hand, although a wide variety of images have been generated using traditional
computer vision algorithms and deep learning models, very little work has been
done on generating the microstructures that are found in abundance in nature. We
have explored two established texture generation algorithms for generating steel
microstructure images: PatchMatch and DCGAN. We have also tried to combine
the reaction-di usion systems with deep learning architectures and have explored
the possibility of its success in generating the steel microstructure images.
