4 citations · 12 across the 14 of their papers we have counts for
21 papers
Image Compression with Isotropic and Anisotropic Shepard Inpainting
Rahul Mohideen Kaja Mohideen, Tobias Alt, Pascal Peter +1
Inpainting-based codecs store sparse selected pixel data and decode by reconstructing the discarded image parts by inpainting. Successful codecs (coders and decoders) traditionally…
Neuroexplicit Diffusion Models for Inpainting of Optical Flow Fields
Tom Fischer, Pascal Peter, Joachim Weickert +1
Deep learning has revolutionized the field of computer vision by introducing large scale neural networks with millions of parameters. Training these networks requires massive datas…
Connecting Image Inpainting with Denoising in the Homogeneous Diffusion Setting
Daniel Gaa, Vassillen Chizhov, Pascal Peter +2
While local methods for image denoising and inpainting may use similar concepts, their connections have hardly been investigated so far. The goal of this work is to establish links…
Efficient Neural Generation of 4K Masks for Homogeneous Diffusion Inpainting
Karl Schrader, Pascal Peter, Niklas Kämper +1
With well-selected data, homogeneous diffusion inpainting can reconstruct images from sparse data with high quality. While 4K colour images of size 3840 x 2160 can already be inpai…
Generalised Scale-Space Properties for Probabilistic Diffusion Models
Pascal Peter
Probabilistic diffusion models enjoy increasing popularity in the deep learning community. They generate convincing samples from a learned distribution of input images with a wide…
Image Blending with Osmosis
Paul Bungert, Pascal Peter, Joachim Weickert
Image blending is an integral part of many multi-image applications such as panorama stitching or remote image acquisition processes. In such scenarios, multiple images are connect…