Generation of Texture: A Case Study with Steel Microstructure Images.

Date of Submission

December 2020

Date of Award

Winter 12-12-2021

Institute Name (Publisher)

Indian Statistical Institute

Document Type

Master's Dissertation

Degree Name

Master of Technology

Subject Name

Computer Science


Electronics and Communication Sciences Unit (ECSU-Kolkata)


Mukherjee, Dipti Prasad (ECSU-Kolkata; ISI)

Abstract (Summary of the Work)

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-diffusion systems have also been successful in generating a wide variety of textures. However, the reaction-diffusion 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-diffusion systems with deep learning architectures and have explored the possibility of its success in generating the steel microstructure images.


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Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


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