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Generating surface topography of paperboard using diffusion models

Loman, Ale LU (2025) In Master's Theses in Mathematical Sciences FMSM01 20242
Mathematical Statistics
Abstract
This thesis uses the unsupervised deep learning method of generative diffusion models to generate images of paperboard surfaces. As the images are intended to be
used as input to various simulations by food packaging company Tetra Pak, simply
looking at them is not sufficient to determine their quality. For this reason, some
new evaluation metrics, specific to paperboard images, are proposed and used. We
present model architectures and training parameters that allow for sampling of new
images, examples of generated images and the evaluation of them. The generated
images bear a close resemblance to real images both when visually inspected and
in terms of the used evaluation metrics. Finally, an alternative use of the models
in this... (More)
This thesis uses the unsupervised deep learning method of generative diffusion models to generate images of paperboard surfaces. As the images are intended to be
used as input to various simulations by food packaging company Tetra Pak, simply
looking at them is not sufficient to determine their quality. For this reason, some
new evaluation metrics, specific to paperboard images, are proposed and used. We
present model architectures and training parameters that allow for sampling of new
images, examples of generated images and the evaluation of them. The generated
images bear a close resemblance to real images both when visually inspected and
in terms of the used evaluation metrics. Finally, an alternative use of the models
in this project is explained. It is possible to use a model to generate images larger than what it was trained on. (Less)
Please use this url to cite or link to this publication:
author
Loman, Ale LU
supervisor
organization
course
FMSM01 20242
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Generative Diffusion Models, Diffusion Models, Generative AI, Paperboard Topography
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMS-3510-2025
ISSN
1404-6342
other publication id
2025:E5
language
English
id
9184892
date added to LUP
2025-02-17 15:49:14
date last changed
2025-02-26 14:37:35
@misc{9184892,
  abstract     = {{This thesis uses the unsupervised deep learning method of generative diffusion models to generate images of paperboard surfaces. As the images are intended to be
used as input to various simulations by food packaging company Tetra Pak, simply
looking at them is not sufficient to determine their quality. For this reason, some
new evaluation metrics, specific to paperboard images, are proposed and used. We
present model architectures and training parameters that allow for sampling of new
images, examples of generated images and the evaluation of them. The generated
images bear a close resemblance to real images both when visually inspected and
in terms of the used evaluation metrics. Finally, an alternative use of the models
in this project is explained. It is possible to use a model to generate images larger than what it was trained on.}},
  author       = {{Loman, Ale}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Generating surface topography of paperboard using diffusion models}},
  year         = {{2025}},
}