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Found 10 papers, 0 papers with code
Deep Spatial and Tonal Data Optimisation for Homogeneous Diffusion Inpainting
This provides real-time performance with high quality results.
CNN-based Euler's Elastica Inpainting with Deep Energy and Deep Image Prior
As a remedy, we design the first neural algorithm that simulates inpainting with Euler's Elastica.
Learning Sparse Masks for Diffusion-based Image Inpainting
Diffusion-based inpainting is a powerful tool for the reconstruction of images from sparse data.
Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods
With only a few small filters, they can achieve the same invariance as existing techniques which require a fine-grained sampling of orientations.
Connections between Numerical Algorithms for PDEs and Neural Networks
We connect these concepts to residual networks, recurrent neural networks, and U-net architectures.
Translating Numerical Concepts for PDEs into Neural Architectures
We investigate what can be learned from translating numerical algorithms into neural networks.
JPEG Meets PDE-based Image Compression
Our codec consistently outperforms JPEG and gives useful indications for successfully developing hybrid codecs further.
Learning Integrodifferential Models for Image Denoising
We show that both multiscale information and anisotropy are crucial for its success.
Translating Diffusion, Wavelets, and Regularisation into Residual Networks
Convolutional neural networks (CNNs) often perform well, but their stability is poorly understood.
Learning a Generic Adaptive Wavelet Shrinkage Function for Denoising
In contrast to many existing shrinkage functions, it is able to enhance image structures by amplifying wavelet coefficients.
