Face illumination normalization based on generative adversarial network

Guo, Dequan; Zhu, Lingrui; Ling, Shenggui; Li, Tianxiang; Zhang, Gexiang; Yang, Qiang; Wang, Ping; Jiang, Shende; Wu, Sidong; Liu, Junbao

doi:10.1007/s11047-022-09892-4

Cited by 2 publications

(1 citation statement)

References 51 publications

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“…1) Brightness level: The use of illumination normalization generative adversarial network -IN-GAN can be well generalized to images with less illumination variations.The method combines deep convolutional neural networks and generative adversarial networks to normalize the illumination of color or grayscale face images, then train feature extractors and classifiers, and process both frontal and non-frontal face images illumination.The method can be extended to other areas, not only for face image generation.However, it cannot preserve more texture details and has some limitations.Meanwhile, the training model is conducted with well-controlled illumination variations, which can deal with poorly controlled illumination variation to a certain extent, but there are still limitations to the study of other features and geometric structures in realistic and complex environments, etc.It can be further investigated whether the model can work better if the model is trained under complex lighting changes. [184].…”

Section: A Global Illuminationmentioning

confidence: 99%

Enhancing image resolution of confocal fluorescence microscopy with deep learning

Huang

Yao

et al. 2023

PhotoniX

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Super-resolution optical imaging is crucial to the study of cellular processes. Current super-resolution fluorescence microscopy is restricted by the need of special fluorophores or sophisticated optical systems, or long acquisition and computational times. In this work, we present a deep-learning-based super-resolution technique of confocal microscopy. We devise a two-channel attention network (TCAN), which takes advantage of both spatial representations and frequency contents to learn a more precise mapping from low-resolution images to high-resolution ones. This scheme is robust against changes in the pixel size and the imaging setup, enabling the optimal model to generalize to different fluorescence microscopy modalities unseen in the training set. Our algorithm is validated on diverse biological structures and dual-color confocal images of actin-microtubules, improving the resolution from ~ 230 nm to ~ 110 nm. Last but not least, we demonstrate live-cell super-resolution imaging by revealing the detailed structures and dynamic instability of microtubules.

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Section: A Global Illuminationmentioning

confidence: 99%

Enhancing image resolution of confocal fluorescence microscopy with deep learning

Huang

Yao

et al. 2023

PhotoniX

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Survey on low-level controllable image synthesis with deep learning

Zhang,

Li,

Yang

2023

era

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<abstract><p>Deep learning, particularly generative models, has inspired controllable image synthesis methods and applications. These approaches aim to generate specific visual content using latent prompts. To explore low-level controllable image synthesis for precise rendering and editing tasks, we present a survey of recent works in this field using deep learning. We begin by discussing data sets and evaluation indicators for low-level controllable image synthesis. Then, we review the state-of-the-art research on geometrically controllable image synthesis, focusing on viewpoint/pose and structure/shape controllability. Additionally, we cover photometrically controllable image synthesis methods for 3D re-lighting studies. While our focus is on algorithms, we also provide a brief overview of related applications, products and resources for practitioners.</p></abstract>

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Face illumination normalization based on generative adversarial network

Cited by 2 publications

References 51 publications

Enhancing image resolution of confocal fluorescence microscopy with deep learning

Enhancing image resolution of confocal fluorescence microscopy with deep learning

Survey on low-level controllable image synthesis with deep learning

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