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 …

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 …

By adding carefully crafted perturbations to input images, adversarial examples (AEs) can
be generated to mislead neural-network-based image classifiers. L2 adversarial …

The filling-in effect of diffusion processes is a powerful tool for various image analysis tasks
such as inpainting-based compression and dense optic flow computation. For noisy data, an …

Optimisation of inpainting data plays an important role in inpainting-based codecs. For
diffusion-based inpainting, it is well-known that a careful data selection has a substantial …

We introduce and discuss shape-based models for finding the best interpolation data in the
compression of images with noise. The aim is to reconstruct missing regions by means of …

Inpainting-based codecs store sparse selected pixel data and decode by reconstructing the
discarded image parts by inpainting. Successful codecs (coders and decoders) traditionally …

In this paper, we study the topic of signal restoration using complexity regularization,
quantifying the compression bit-cost of the signal estimate. While complexity-regularized …

Inpainting-based compression represents images in terms of a sparse subset of its pixel
data. Storing the carefully optimised positions of known data creates a lossless compression …