Results 21 to 30 of about 1,132,388 (176)
Rethinking Deep Image Prior for Denoising [PDF]
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 Deep image prior (DIP) serves as a good inductive bias for diverse inverse problems. Among them, denoising is known to be particularly challenging for the DIP due to noise fitting with the requirement of an early stopping. To address the issue, we first analyze the DIP by the notion of effective degrees of freedom (DF) to monitor the optimization ...
Jo, Yeonsik +2 more
openaire +2 more sources
Combining Deep Image Prior and Second-Order Generalized Total Variance for Image Inpainting
Mathematics, 2023 Image inpainting is a crucial task in computer vision that aims to restore missing and occluded parts of damaged images. Deep-learning-based image inpainting methods have gained popularity in recent research.
Shaopei You +4 more
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
Object Level Deep Feature Pooling for Compact Image Representation [PDF]
, 2015 Convolutional Neural Network (CNN) features have been successfully employed in recent works as an image descriptor for various vision tasks. But the inability of the deep CNN features to exhibit invariance to geometric transformations and object ...
Babu, R. Venkatesh, Mopuri, Konda Reddy
core +1 more source
Image Reconstruction via Deep Image Prior Subspaces
, 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 ...
Barbano, Riccardo +5 more
openaire +2 more sources