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of 10 586
pro vyhledávání: '"internal learning"'
Equivariant Imaging (EI) regularization has become the de-facto technique for unsupervised training of deep imaging networks, without any need of ground-truth data. Observing that the EI-based unsupervised training paradigm currently has significant
Externí odkaz:
http://arxiv.org/abs/2411.05771
Diffusion models are now the undisputed state-of-the-art for image generation and image restoration. However, they require large amounts of computational power for training and inference. In this paper, we propose lightweight diffusion models for ima
Externí odkaz:
http://arxiv.org/abs/2406.04206
Deep learning, in general, focuses on training a neural network from large labeled datasets. Yet, in many cases there is value in training a network just from the input at hand. This is particularly relevant in many signal and image processing proble
Externí odkaz:
http://arxiv.org/abs/2312.07425
In this paper, we present a novel robust framework for low-level vision tasks, including denoising, object removal, frame interpolation, and super-resolution, that does not require any external training data corpus. Our proposed approach directly lea
Externí odkaz:
http://arxiv.org/abs/2312.07835
Publikováno v:
IEEE Access, Vol 12, Pp 162285-162298 (2024)
In recent years, deep convolutional neural networks (CNNs) have significantly improved pansharpening performance compared to traditional methods. However, existing CNN-based methods for pansharpening still lack spatial detail and suffer from spectral
Externí odkaz:
https://doaj.org/article/26b8494162f246e6a02990e95ece1f92
Autor:
Goyes, Paul, Vargas, Edwin, Correa, Claudia, Sun, Yu, Kamilov, Ulugbek, Wohlberg, Brendt, Arguello, Henry
Physical and budget constraints often result in irregular sampling, which complicates accurate subsurface imaging. Pre-processing approaches, such as missing trace or shot interpolation, are typically employed to enhance seismic data in such cases. R
Externí odkaz:
http://arxiv.org/abs/2211.11889
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Akademický článek
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Internal learning for single-image generation is a framework, where a generator is trained to produce novel images based on a single image. Since these models are trained on a single image, they are limited in their scale and application. To overcome
Externí odkaz:
http://arxiv.org/abs/2110.02900
Publikováno v:
In Neurocomputing 28 April 2023 531:61-73