4 citations · 7 across the 7 of their papers we have counts for
12 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…
Deep Spatial and Tonal Data Optimisation for Homogeneous Diffusion Inpainting
Pascal Peter, Karl Schrader, Tobias Alt +1
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 particu…
CNN-based Euler's Elastica Inpainting with Deep Energy and Deep Image Prior
Karl Schrader, Tobias Alt, Joachim Weickert +1
Euler's elastica constitute an appealing variational image inpainting model. It minimises an energy that involves the total variation as well as the level line curvature. These com…
Learning Sparse Masks for Diffusion-based Image Inpainting
Tobias Alt, Pascal Peter, Joachim Weickert
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…
Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods
Tobias Alt, Karl Schrader, Joachim Weickert +2
Partial differential equation (PDE) models and their associated variational energy formulations are often rotationally invariant by design. This ensures that a rotation of the inpu…
Connections between Numerical Algorithms for PDEs and Neural Networks
Tobias Alt, Karl Schrader, Matthias Augustin +2
We investigate numerous structural connections between numerical algorithms for partial differential equations (PDEs) and neural architectures. Our goal is to transfer the rich set…
Translating Numerical Concepts for PDEs into Neural Architectures
Tobias Alt, Pascal Peter, Joachim Weickert +1
We investigate what can be learned from translating numerical algorithms into neural networks. On the numerical side, we consider explicit, accelerated explicit, and implicit schem…
JPEG Meets PDE-based Image Compression
Sarah Andris, Joachim Weickert, Tobias Alt +1
Inpainting-based image compression is emerging as a promising competitor to transform-based compression techniques. Its key idea is to reconstruct image information from only few k…
Compressing Colour Images with Joint Inpainting and Prediction
Rahul Mohideen Kaja Mohideen, Pascal Peter, Tobias Alt +2
Inpainting-based codecs store sparse, quantised pixel data directly and decode by interpolating the discarded image parts. This interpolation can be used simultaneously for efficie…
Learning Integrodifferential Models for Image Denoising
Tobias Alt, Joachim Weickert
We introduce an integrodifferential extension of the edge-enhancing anisotropic diffusion model for image denoising. By accumulating weighted structural information on multiple sca…
Translating Diffusion, Wavelets, and Regularisation into Residual Networks
Tobias Alt, Joachim Weickert, Pascal Peter
Convolutional neural networks (CNNs) often perform well, but their stability is poorly understood. To address this problem, we consider the simple prototypical problem of signal de…
Learning a Generic Adaptive Wavelet Shrinkage Function for Denoising
Tobias Alt, Joachim Weickert
The rise of machine learning in image processing has created a gap between trainable data-driven and classical model-driven approaches: While learning-based models often show super…