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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
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Binary Noise Guidance Learning for Remote Sensing Image-to-Image Translation
Remote Sensing, 2023 Image-to-image translation (I2IT) is an important visual task that aims to learn a mapping of images from one domain to another while preserving the representation of the content. The phenomenon known as mode collapse makes this task challenging.
Guoqing Zhang +3 more
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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
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Unsupervised Image to Image Translation With Additional Mask
IEEE Access, 2023 With the development of deep learning, the performance of image-to-image translation is also increasing. However, most of the image-to-image translation models depend on the implicit method which does not explain why the models alter specific parts of ...
Hyun-Tae Choi +2 more
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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
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UMGAN: Underwater Image Enhancement Network for Unpaired Image-to-Image Translation
Journal of Marine Science and Engineering, 2023 Due to light absorption and scattering underwater images suffer from low contrast, color distortion, blurred details, and uneven illumination, which affect underwater vision tasks and research.
Boyang Sun +3 more
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Consistent Embedded GAN for Image-to-Image Translation
IEEE Access, 2019 Generative Adversarial Networks (GANs) have achieved remarkable progress in image-to-image translation tasks. However, these methods have the common problem that lacking the ability to generate both perceptually realistic and diverse images in the target
Feng Xiong, Qianqian Wang, Quanxue Gao
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4K Real Time Image to Image Translation Network With Transformers
IEEE Access, 2022 CNNs have traditionally been applied in computer vision. Recently, applying Transformer networks, originally a technique in natural language processing, to computer vision has received much attention and produced superior results.
Kei Shibasaki +2 more
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Implicit pairs for boosting unpaired image-to-image translation
Visual Informatics, 2020 In image-to-image translation the goal is to learn a mapping from one image domain to another. In the case of supervised approaches the mapping is learned from paired samples.
Yiftach Ginger +3 more
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Unsupervised many‐to‐many image‐to‐image translation across multiple domains
IET Image Processing, 2021 Unsupervised multi‐domain image‐to‐image translation aims to synthesize images among multiple domains without labelled data, which is more general and complicated than one‐to‐one image mapping. However, existing methods mainly focus on reducing the large
Ye Lin +3 more
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