Image Reconstruction Using PDE, Variational and Regularization Methods
Date
2013
Authors
P, Jidesh
Journal Title
Journal ISSN
Volume Title
Publisher
National Institute of Technology Karnataka, Surathkal
Abstract
Image restoration and enhancement are two important requirements in
the field of image processing. In this study three anisotropic non-linear
diffusion filters are proposed for image restoration and enhancement and
one filter for image inpainting. The orientation, type and extent of filtering are controlled by the decision mechanism based on the underlying
image features. The first process is a conditionally anisotropic diffusion for
deblurring and denoising images. This process is a time-dependent curvature based model and the steady state is attained at a faster rate, using
the explicit time-marching scheme. The filter switches between isotropic
and anisotropic behavior based on the local image features. Two other
non-linear curvature based diffusion processes are devised, one for image
enhancement and the other one for image inpainting. The diffusion process
in these filters is driven by the Gauss curvature of the level curves of the
image. Therefore, these methods are capable of preserving structures even
with non-zero mean curvature values like curvy edges and corners. To be
precise, the second process couples a hyperbolic shock filter together with
a Gauss curvature driven diffusion term to enhance images. And the third
one inpaints the intended domain based on the Gauss curvature. Finally, a
fourth-order shock coupled diffusion filter is proposed for image enhancement. This is an anisotropic model that converges at a faster rate and
preserves planar approximation while enhancing images. In this study a
thorough theoretical and experimental analysis is carried out for each and
every diffusion process introduced as a part of this thesis work. A variety
of applications are presented for denoising and deblurring gray-level and
color images. The required mathematical preliminaries are presented in
the introduction of the thesis. We conclude the thesis highlighting some of
the future enhancements that could be possibly taken forward for further
research.
Description
Keywords
Department of Mathematical and Computational Sciences, Image Reconstruction, Image enhancement, Image inpainting, Variational methods, Regularization methods, PDE methods