Zobrazeno 1 - 10
of 544
pro vyhledávání: '"Weickert, Joachim"'
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
Deep learning has revolutionized the field of computer vision by introducing large scale neural networks with millions of parameters. Training these networks requires massive datasets and leads to intransparent models that can fail to generalize. At
Externí odkaz:
http://arxiv.org/abs/2405.14599
Homogeneous diffusion inpainting can reconstruct missing image areas with high quality from a sparse subset of known pixels, provided that their location as well as their gray or color values are well optimized. This property is exploited in inpainti
Externí odkaz:
http://arxiv.org/abs/2401.06747
In recent years inpainting-based compression methods have been shown to be a viable alternative to classical codecs such as JPEG and JPEG2000. Unlike transform-based codecs, which store coefficients in the transform domain, inpainting-based approache
Externí odkaz:
http://arxiv.org/abs/2401.06744
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 between the two by focusing on the most foundational scenario on both
Externí odkaz:
http://arxiv.org/abs/2309.13486
Autor:
Schaefer, Kristina, Weickert, Joachim
We introduce regularised diffusion--shock (RDS) inpainting as a modification of diffusion--shock inpainting from our SSVM 2023 conference paper. RDS inpainting combines two carefully chosen components: homogeneous diffusion and coherence-enhancing sh
Externí odkaz:
http://arxiv.org/abs/2309.08761
Anisotropic diffusion processes with a diffusion tensor are important in image analysis, physics, and engineering. However, their numerical approximation has a strong impact on dissipative artefacts and deviations from rotation invariance. In this wo
Externí odkaz:
http://arxiv.org/abs/2309.05575
Publikováno v:
In L. Calatroni, M. Donatelli, S. Morigi, M. Prato, M. Santavesaria (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 14009, Springer, Cham, 16-28, 2023
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 inpainted in real time, optimising the known data for applications like ima
Externí odkaz:
http://arxiv.org/abs/2303.10096
Autor:
Schaefer, Kristina, Weickert, Joachim
Publikováno v:
In L. Calatroni, M. Donatelli, S. Morigi, M. Prato, M. Santacesaria (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 14009. Springer, Cham, 588-600, 2023
We propose diffusion-shock (DS) inpainting as a hitherto unexplored integrodifferential equation for filling in missing structures in images. It combines two carefully chosen components that have proven their usefulness in different applications: hom
Externí odkaz:
http://arxiv.org/abs/2303.09450
Publikováno v:
In L. Calatroni, M. Donatelli, S. Morigi, M. Prato, M. Santavesaria (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 14009, Springer, Cham, 652-664, 2023
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 connected at predefined boundaries to form a larger image. A convincing trans
Externí odkaz:
http://arxiv.org/abs/2303.07762