Results 21 to 30 of about 97,810,316 (272)

Preserving spatial and quantitative information in unpaired biomedical image-to-image translation

Cell Reports: Methods
Summary: Analysis of biological samples often requires integrating diverse imaging modalities to gain a comprehensive understanding. While supervised biomedical image translation methods have shown success in synthesizing images across different ...
Joshua Yedam You, Minho Eom, Tae-Ik Choi, Eun-Seo Cho, Jieun Choi, Minyoung Lee, Changyeop Shin, Jieun Moon, Eunji Kim, Pilhan Kim, Cheol-Hee Kim, Young-Gyu Yoon   +11 more
doaj   +2 more sources

Weakly Supervised Learning for Transmission Line Detection Using Unpaired Image-to-Image Translation

Remote Sensing, 2022
To achieve full autonomy of unmanned aerial vehicles (UAVs), obstacle detection and avoidance are indispensable parts of visual recognition systems. In particular, detecting transmission lines is an important topic due to the potential risk of accidents ...
Jiho Choi, Sang Jun Lee
doaj   +2 more sources

Denoising diffusion-based MRI to CT image translation enables automated spinal segmentation [PDF]

European Radiology Experimental, 2023
Background Automated segmentation of spinal magnetic resonance imaging (MRI) plays a vital role both scientifically and clinically. However, accurately delineating posterior spine structures is challenging.
Robert Graf, Joachim Schmitt, Sarah Schlaeger, Hendrik Kristian Möller, Vasiliki Sideri-Lampretsa, Anjany Sekuboyina, Sandro Manuel Krieg, Benedikt Wiestler, Bjoern Menze, Daniel Rueckert, Jan Stefan Kirschke   +10 more
doaj   +2 more sources

Homomorphic Interpolation Network for Unpaired Image-to-image Translation

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020
Generative adversarial networks have achieved great success in unpaired image-to-image translation. Cycle consistency, a key component for this task, allows modeling the relationship between two distinct domains without paired data. In this paper, we propose an alternative framework, as an extension of latent space interpolation, to consider the ...
Ying-Cong Chen, Jiaya Jia
openaire   +4 more sources

Latent-SDE: guiding stochastic differential equations in latent space for unpaired image-to-image translation

Complex & Intelligent Systems
Score-based diffusion models have shown promising results in unpaired image-to-image translation (I2I). However, the existing methods only perform unpaired I2I in pixel space, which requires high computation costs.
Xianjie Zhang, Min Li, Yujie He, Yao Gou, Yusen Zhang   +4 more
doaj   +2 more sources

Homomorphic Latent Space Interpolation for Unpaired Image-To-Image Translation

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Generative adversarial networks have achieved great success in unpaired image-to-image translation. Cycle consistency allows modeling the relationship between two distinct domains without paired data. In this paper, we propose an alternative framework, as an extension of latent space interpolation, to consider the intermediate region between two ...
Ying-Cong Chen, Xiaogang Xu 0002, Zhuotao Tian, Jiaya Jia   +3 more
openaire   +3 more sources

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, Yupeng Mei, Ni Yan, Yingyi Chen   +3 more
doaj   +1 more source

Enhanced Unpaired Image-to-Image Translation via Transformation in Saliency Domain

IEEE Access, 2023
Unpaired image to image translation is the task of converting images in unpaired datasets. The primary goal of the task is to translate a source image into the image aligned with the target domain while keeping the fundamental content.
Kei Shibasaki, Masaaki Ikehara
doaj   +1 more source

Contrastive Learning for Unpaired Image-to-Image Translation [PDF]

, 2020
In image-to-image translation, each patch in the output should reflect the content of the corresponding patch in the input, independent of domain. We propose a straightforward method for doing so -- maximizing mutual information between the two, using a framework based on contrastive learning.
Taesung Park, Alexei A. Efros, Richard Zhang 0001, Jun-Yan Zhu   +3 more
openaire   +3 more sources

ITTR: Unpaired Image-to-Image Translation with Transformers

CoRR, 2022
Unpaired image-to-image translation is to translate an image from a source domain to a target domain without paired training data. By utilizing CNN in extracting local semantics, various techniques have been developed to improve the translation performance.
Wanfeng Zheng, Qiang Li 0024, Guoxin Zhang, Pengfei Wan 0001, Zhongyuan Wang 0006   +4 more
openaire   +2 more sources