Zobrazeno 1 - 10
of 128
pro vyhledávání: '"Alt, Tobias"'
Inpainting-based codecs store sparse selected pixel data and decode by reconstructing the discarded image parts by inpainting. Successful codecs (coders and decoders) traditionally use inpainting operators that solve partial differential equations. T
Externí odkaz:
http://arxiv.org/abs/2406.06247
Autor:
Alt, Tobias, Ibisch, Andrea, Meiser, Clemens, Wilhelm, Anna, Zimmer, Raphael, Berghoff, Christian, Droste, Christoph, Karschau, Jens, Laus, Friederike, Plaga, Rainer, Plesch, Carola, Sennewald, Britta, Thaeren, Thomas, Unverricht, Kristina, Waurick, Steffen
Generative AI models are capable of performing a wide range of tasks that traditionally require creativity and human understanding. They learn patterns from existing data during training and can subsequently generate new content such as texts, images
Externí odkaz:
http://arxiv.org/abs/2406.04734
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 particularly useful for applications such as image compression, where the ori
Externí odkaz:
http://arxiv.org/abs/2208.14371
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 components are transparent and make it attractive for shape completion ta
Externí odkaz:
http://arxiv.org/abs/2207.07921
Publikováno v:
In Human Movement Science December 2024 98
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 location -- the inpainting mask -- is challenging. A commonly used too
Externí odkaz:
http://arxiv.org/abs/2110.02636
Partial differential equation (PDE) models and their associated variational energy formulations are often rotationally invariant by design. This ensures that a rotation of the input results in a corresponding rotation of the output, which is desirabl
Externí odkaz:
http://arxiv.org/abs/2108.13993
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 mathematical foundations from the world of PDEs to neural networks
Externí odkaz:
http://arxiv.org/abs/2107.14742
We investigate what can be learned from translating numerical algorithms into neural networks. On the numerical side, we consider explicit, accelerated explicit, and implicit schemes for a general higher order nonlinear diffusion equation in 1D, as w
Externí odkaz:
http://arxiv.org/abs/2103.15419
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 known regions through inpainting. Specific partial differential equatio
Externí odkaz:
http://arxiv.org/abs/2102.01138