Results 71 to 80 of about 5,457 (203)

Generative Artificial Intelligence for Computer Vision in Endodontics: A Review of Current State and Future Potential

International Endodontic Journal, EarlyView.
ABSTRACT Background Computer vision methods based on artificial intelligence (AI) have found numerous applications in endodontic diagnosis and treatment planning. While most current applications employ discriminative deep learning models for detection and classification tasks, the field is now witnessing the rise of generative AI (GenAI), a class of AI
Hossein Mohammad‐Rahimi, Sanyam Jain, Khuram Naveed, Sepanta Hosseinpour, Lise‐Lotte Kirkevang, Ali Nosrat, Ruben Pauwels   +6 more
wiley   +1 more source

Multi-state image generation of complex landscapes via semantic category style transfer

Dianzi Jishu Yingyong
Complex landscape images contain various objects with different characteristics, and traditional style transfer methods are unable to perform local style transfer on different objects within the same image.
Sang Chenhao, Mo Lufeng, Tu Guoqing
doaj   +1 more source

CycleGAN with three different unpaired datasets

CoRR, 2022
The original publication Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks served as the inspiration for this implementation project. Researchers developed a novel method for doing image-to-image translations using an unpaired dataset in the original study.
openaire   +3 more sources

A Method for Dehazing Based on CycleGAN

Journal of Physics: Conference Series, 2022
Abstract With the development of artificial intelligence, computer vision has been widely applicated. The performance of these applications is closely related to the image quality, which is, however, affected by various lighting and weather conditions, such as rain and haze.
Yue Hu, Chunyu Chen, Siyi Hou, Menglong Yang   +3 more
openaire   +1 more source

Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection.
Xu Wang, Daeun Choi, Damian Adams, Jaehyun Ahn, Kelly Balmant, Raquel Dias, Carlos D. Messina, Rafael Muñoz‐Carpena, Vance M. Whitaker, Haipeng Yu, Ziwen Yu, Chang Zhao, Changying Li   +12 more
wiley   +1 more source

Fully Automated Electronic Cleansing Using CycleGAN in Computed Tomography Colonography

, 2022
In computed tomography colonography (CTC), an electric cleansing technique is used to mix barium with residual fluid, and colon residue is removed by image processing. However, a nonhomogenous mixture of barium and residue may not be properly removed. We
Hiroshi Fujita, Yoshitaka Isobe, Fujio Morita, Atsushi Teramoto, Kuniaki Saito   +4 more
core   +1 more source

Local Binary Pattern–Cycle Generative Adversarial Network Transfer: Transforming Image Style from Day to Night

Journal of Imaging
Transforming images from day style to night style is crucial for enhancing perception in autonomous driving and smart surveillance. However, existing CycleGAN-based approaches struggle with texture loss, structural inconsistencies, and high computational
Abeer Almohamade, Salma Kammoun, Fawaz Alsolami   +2 more
doaj   +1 more source

Synthetic MRI from radiotherapy planning CT images for Head‐and‐Neck Cancer

Journal of Applied Clinical Medical Physics, Volume 27, Issue 11, November 2026.
Abstract Background Computed Tomography (CT) provides geometric accuracy and electron density information essential for radiotherapy planning; however, it offers limited soft‐tissue contrast. Magnetic Resonance Imaging (MRI) improves tumor visualization but introduces registration uncertainties when acquired separately from the radiotherapy planning CT
Prabhakar Ramachandran, Akshay C Dhargave, Catherine Jones, Dionee Liefman, Lachlan McDowell, Howard Liu   +5 more
wiley   +1 more source

IE-CycleGAN: improved cycle consistent adversarial network for unpaired PET image enhancement. [PDF]

PURPOSE Technological advances in instruments have greatly promoted the development of positron emission tomography (PET) scanners. State-of-the-art PET scanners such as uEXPLORER can collect PET images of significantly higher quality.
Cui, Jianan, Su, Xinhui, Liu, Huafeng, Shi, Kuangyu, Luo, Yi, Chen, Donghe   +5 more
core   +1 more source

Towards a Robust Thermal-Visible Heterogeneous Face Recognition Approach Based on a Cycle Generative Adversarial Network.

International Journal of Interactive Multimedia and Artificial Intelligence, 2022
Security is a sensitive area that concerns all authorities around the world due to the emerging terrorism phenomenon. Contactless biometric technologies such as face recognition have grown in interest for their capacity to identify probe subjects ...
Nadir Kamel Benamara, Ehlem Zigh, Tarik Boudghene Stambouli, Mokhtar Keche   +3 more
doaj   +1 more source