Results 31 to 40 of about 225,507 (258)
Defending Against Adversarial Fingerprint Attacks Based on Deep Image Prior
IEEE Access, 2023 Recently, deep learning-based biometric authentication systems, especially fingerprint authentication, have been used widely in real-world. However, these systems are vulnerable to adversarial attacks which prevent deep learning models from ...
Hwajung Yoo +4 more
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Hyperspectral Denoising Using Asymmetric Noise Modeling Deep Image Prior
Remote Sensing, 2023 Deep image prior (DIP) is a powerful technique for image restoration that leverages an untrained network as a handcrafted prior. DIP can also be used for hyperspectral image (HSI) denoising tasks and has achieved impressive performance.
Yifan Wang +5 more
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Sensors, 2023 Two-dimensional observation of biological samples at hundreds of nanometers resolution or even below is of high interest for many sensitive medical applications. Recent advances have been obtained over the last ten years with computational imaging. Among
Lyes Bouchama +3 more
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Deep Image Prior for medical image denoising, a study about parameter initialization
Frontiers in Applied Mathematics and Statistics, 2022 Convolutional Neural Networks are widely known and used architectures in image processing contexts, in particular for medical images. These Deep Learning techniques, known for their ability to extract high-level features, almost always require a labeled ...
Davide Sapienza +6 more
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Deep Tensor Attention Prior Network for Hyperspectral Image Denoising
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Hyperspectral imaging techniques can generate continuous narrowband images with a high spectral resolution. However, owing to environmental disturbances, atmospheric effects, and hardware limitations of hyperspectral imaging sensors, captured ...
Weilin Shen +3 more
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Combining Deep Image Prior and Second-Order Generalized Total Variance for Image Denoising
IEEE Access, 2023 Deep image prior is a classical unsupervised deep learning method that does not require plenty of training samples, because in some practical applications, like medical imaging, collecting tons of training samples is not always viable.
Jianlou Xu +3 more
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Image Reconstruction via Deep Image Prior Subspaces
Trans. Mach. Learn. Res., 2023 Deep learning has been widely used for solving image reconstruction tasks but its deployability has been held back due to the shortage of high-quality training data. Unsupervised learning methods, such as the deep image prior (DIP), naturally fill this gap, but bring a host of new issues: the susceptibility to overfitting due to a lack of robust early ...
Riccardo Barbano +5 more
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Multi-dynamic deep image prior for cardiac MRI. [PDF]
Magn Reson MedAbstractPurposeCardiovascular magnetic resonance imaging is a powerful diagnostic tool for assessing cardiac structure and function. However, traditional breath‐held imaging protocols pose challenges for patients with arrhythmias or limited breath‐holding capacity.
Vornehm M +6 more
europepmc +7 more sources
Deep learning-based image recognition for autonomous driving
IATSS Research, 2019 Various image recognition tasks were handled in the image recognition field prior to 2010 by combining image local features manually designed by researchers (called handcrafted features) and machine learning method. After entering the 2010, However, many
Hironobu Fujiyoshi +2 more
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Learning Deep Image Priors for Blind Image Denoising [PDF]
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019 Image denoising is the process of removing noise from noisy images, which is an image domain transferring task, i.e., from a single or several noise level domains to a photo-realistic domain. In this paper, we propose an effective image denoising method by learning two image priors from the perspective of domain alignment.
Xianxu Hou +7 more
openaire +2 more sources