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Unsupervised Image-to-Image Translation: A Review [PDF]
Sensors, 2022 Supervised image-to-image translation has been proven to generate realistic images with sharp details and to have good quantitative performance.
Henri Hoyez +4 more
doaj +4 more sources
Unsupervised Exemplar-Domain Aware Image-to-Image Translation [PDF]
Entropy, 2021 Image-to-image translation is used to convert an image of a certain style to another of the target style with the original content preserved. A desired translator should be capable of generating diverse results in a controllable many-to-many fashion.
Yuanbin Fu, Jiayi Ma, Xiaojie Guo
doaj +5 more sources
All-in-one medical image-to-image translation [PDF]
Cell Reports: MethodsSummary: The growing availability of public multi-domain medical image datasets enables training omnipotent image-to-image (I2I) translation models. However, integrating diverse protocols poses challenges in domain encoding and scalability. Therefore, we
Luyi Han +12 more
doaj +7 more sources
Towards Instance-level Image-to-Image Translation [PDF]
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 Unpaired Image-to-image Translation is a new rising and challenging vision problem that aims to learn a mapping between unaligned image pairs in diverse domains.
Huang, Mingyang +4 more
core +2 more sources
Is image-to-image translation the panacea for multimodal image registration? A comparative study [PDF]
PLoS ONE, 2022 Despite current advancement in the field of biomedical image processing, propelled by the deep learning revolution, multimodal image registration, due to its several challenges, is still often performed manually by specialists.
Jiahao Lu +3 more
doaj +3 more sources
High-Resolution Semantically Consistent Image-to-Image Translation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Deep learning has become one of remote sensing scientists' most efficient computer vision tools in recent years. However, the lack of training labels for the remote sensing datasets means that scientists need to solve the domain adaptation (DA ...
Mikhail Sokolov +5 more
doaj +3 more sources
MRI Cross-Modality Image-to-Image Translation. [PDF]
Sci Rep, 2020 AbstractWe present a cross-modality generation framework that learns to generate translated modalities from given modalities in MR images. Our proposed method performs Image Modality Translation (abbreviated as IMT) by means of a deep learning model that leverages conditional generative adversarial networks (cGANs).
Yang Q +5 more
europepmc +4 more sources
Image to Image Translation for Domain Adaptation [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 We propose a general framework for unsupervised domain adaptation, which allows deep neural networks trained on a source domain to be tested on a different target domain without requiring any training annotations in the target domain. This is achieved by
Kim, Kyungnam +4 more
core +2 more sources
Reversible GANs for Memory-efficient Image-to-Image Translation [PDF]
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 The Pix2pix and CycleGAN losses have vastly improved the qualitative and quantitative visual quality of results in image-to-image translation tasks. We extend this framework by exploring approximately invertible architectures which are well suited to ...
van der Ouderaa, Tycho F. A. +1 more
core +4 more sources
Hubble Meets Webb: Image-to-Image Translation in Astronomy [PDF]
SensorsThis work explores the generation of James Webb Space Telescope (JWSP) imagery via image-to-image translation from the available Hubble Space Telescope (HST) data.
Vitaliy Kinakh +6 more
doaj +2 more sources