[HTML][HTML] Improving road surface area extraction via semantic segmentation with conditional generative learning for deep inpainting operations
The road surface area extraction task is generally carried out via semantic segmentation
over remotely-sensed imagery. However, this supervised learning task is often costly as it …
over remotely-sensed imagery. However, this supervised learning task is often costly as it …
“Seeing” Beneath the Clouds—Machine‐Learning‐Based Reconstruction of North African Dust Plumes
F Kanngießer, S Fiedler - AGU Advances, 2024 - Wiley Online Library
Mineral dust is one of the most abundant atmospheric aerosol species and has various far‐
reaching effects on the climate system and adverse impacts on air quality. Satellite …
reaching effects on the climate system and adverse impacts on air quality. Satellite …
No ground truth needed: unsupervised sinogram inpainting for nanoparticle electron tomography (UsiNet) to correct missing wedges
Complex natural and synthetic materials, such as subcellular organelles, device
architectures in integrated circuits, and alloys with microstructural domains, require …
architectures in integrated circuits, and alloys with microstructural domains, require …
In-distribution interpretability for challenging modalities
It is widely recognized that the predictions of deep neural networks are difficult to parse
relative to simpler approaches. However, the development of methods to investigate the …
relative to simpler approaches. However, the development of methods to investigate the …
Self-Supervised Learning for Text Recognition: A Critical Survey
C Penarrubia, JJ Valero-Mas… - arXiv preprint arXiv …, 2024 - arxiv.org
Text Recognition (TR) refers to the research area that focuses on retrieving textual
information from images, a topic that has seen significant advancements in the last decade …
information from images, a topic that has seen significant advancements in the last decade …
An effective LRTC model integrated with total α‐order variation and boundary adjustment for multichannel visual data inpainting
X Yang, Y Xue, Z Lv, H Jin - IET Image Processing, 2022 - Wiley Online Library
Restoring damaged multichannel visual data with high loss ratio is quite a challenging task.
To address this problem, an effective LRTC (low‐rank tensor completion) model integrated …
To address this problem, an effective LRTC (low‐rank tensor completion) model integrated …
A method for face image inpainting based on autoencoder and generative adversarial network
Face image inpainting has great value in the fields of computer vision and digital image
processing. In this paper, we propose a face image inpainting method based on …
processing. In this paper, we propose a face image inpainting method based on …
Data-driven metal artifact correction in computed tomography using conditional generative adversarial networks
Metal objects in the field of view cause artifacts in the image, which manifest as dark and
bright streaks and degrade the diagnostic value of the image. Standard approaches for …
bright streaks and degrade the diagnostic value of the image. Standard approaches for …
“Seeing” beneath the clouds-machine-learning-based reconstruction of North African dust events
F Kanngießer, S Fiedler - ESS Open Archive, 2023 - oceanrep.geomar.de
Mineral dust is one of the most abundant atmospheric aerosol species and has various far-
reaching effects on the climate system and adverse impacts on air quality. Satellite …
reaching effects on the climate system and adverse impacts on air quality. Satellite …
Contribution to Object Extraction in Cartography: A Novel Deep Learning-Based Solution to Recognise, Segment and Post-Process the Road Transport Network as a …
CI Cira - 2022 - oa.upm.es
Remote sensing imagery combined with deep learning strategies is often regarded as an
ideal solution for interpreting scenes and monitoring infrastructures with remarkable …
ideal solution for interpreting scenes and monitoring infrastructures with remarkable …