We investigate numerous structural connections between numerical algorithms for partial
differential equations (PDEs) and neural architectures. Our goal is to transfer the rich set of …

We consider curvature depending variational models for image regularization, such as
Euler's elastica. These models are known to provide strong priors for the continuity of edges …

The work of Levin et al.(2004) popularized stroke-based methods that add color to gray
value images according to a small amount of user-specified color samples. Even though …

Diffusion-based inpainting is a powerful tool for the reconstruction of images from sparse
data. Its quality strongly depends on the choice of known data. Optimising their spatial …

In recent years, the diffusion-based image compression scheme has achieved significant
success, which inspires us to use diffusion theory to employ the diffusion model for point …

Harmonic inpainting with optimised data is very popular for inpainting-based image
compression. We improve this approach in three important aspects. Firstly, we replace the …

Plug-and-Play priors recently emerged as a powerful technique for solving inverse problems
by plugging a denoiser into a classical optimization algorithm. The denoiser accounts for the …

Diffusion-based inpainting can reconstruct missing image areas with high quality from
sparse data, provided that their location and their values are well optimised. This is …

Digital image inpainting refers to techniques used to reconstruct a damaged or incomplete
image by exploiting available image information. The main goal of this work is to perform the …

Inpainting-based compression methods are qualitatively promising alternatives to transform-
based codecs, but they suffer from the high computational cost of the inpainting step. This …