Results 71 to 80 of about 1,132,388 (176)

DeepEIT: Deep Image Prior Enabled Electrical Impedance Tomography

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Neural networks (NNs) have been widely applied in tomographic imaging through data-driven training and image processing. One of the main challenges in using NNs in real medical imaging is the requirement of massive amounts of training data - which are not always available in clinical practice.
Dong Liu, Junwu Wang, Qianxue Shan, Danny Smyl, Jiansong Deng, Jiangfeng Du   +5 more
openaire   +2 more sources

Self-Supervised Deep Hyperspectral Inpainting with Plug-and-Play and Deep Image Prior Models

Remote Sensing
Hyperspectral images are typically composed of hundreds of narrow and contiguous spectral bands, each containing information regarding the material composition of the imaged scene.
Shuo Li, Mehrdad Yaghoobi
doaj   +1 more source

Learning from Multiple Instances: A Two-Stage Unsupervised Image Denoising Framework Based on Deep Image Prior

Applied Sciences, 2022
Supervised image denoising methods based on deep neural networks require a large amount of noisy-clean or noisy image pairs for network training. Thus, their performance drops drastically when the given noisy image is significantly different from the ...
Shaoping Xu, Xiaojun Chen, Yiling Tang, Shunliang Jiang, Xiaohui Cheng, Nan Xiao   +5 more
doaj   +1 more source

Understanding Neural Pathways in Zebrafish through Deep Learning and High Resolution Electron Microscope Data

, 2018
The tracing of neural pathways through large volumes of image data is an incredibly tedious and time-consuming process that significantly encumbers progress in neuroscience.
Bushman, Kristi, Nyawira, Ishtar, Qian, Iris, Zhang, Annie   +3 more
core   +1 more source

Domain and Geometry Agnostic CNNs for Left Atrium Segmentation in 3D Ultrasound

, 2018
Segmentation of the left atrium and deriving its size can help to predict and detect various cardiovascular conditions. Automation of this process in 3D Ultrasound image data is desirable, since manual delineations are time-consuming, challenging and ...
A Rohner, AT Hoven van den, C Baur, C Knackstedt, K Kamnitsas, N Almeida, O Oktay, RM Lang, RR Buechel   +8 more
core   +1 more source

R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections

, 2018
The influence of Deep Learning on image identification and natural language processing has attracted enormous attention globally. The convolution neural network that can learn without prior extraction of features fits well in response to the rapid ...
Huang, TonTon Hsien-De, Kao, Hung-Yu
core   +1 more source

Mean-Curvature-Regularized Deep Image Prior with Soft Attention for Image Denoising and Deblurring

Mathematics
Sparsity-driven regularization has undergone significant development in single-image restoration, particularly with the transition from handcrafted priors to trainable deep architectures.
Muhammad Israr, Shahbaz Ahmad, Muhammad Nabeel Asghar, Saad Arif   +3 more
doaj   +1 more source

Deep Neural Network for Image Super Resolution Driven by Prior Denoising

Nantong Daxue xuebao. Ziran kexue ban, 2021
In order to improve image super resolution, a double layer convolution neural network in image denoising is embedded in image restoration tasks. The image super resolution method driven by prior denoising with deep neural network is proposed.
CHENG Fanqiang;ZHU Yonggui;, ZHU Yonggui
doaj   +1 more source

Hyperspectral Image Denoising by Pixel-Wise Noise Modeling and TV-Oriented Deep Image Prior

Remote Sensing
Model-based hyperspectral image (HSI) denoising methods have attracted continuous attention in the past decades, due to their effectiveness and interpretability.
Lixuan Yi, Qian Zhao, Zongben Xu
doaj   +1 more source

Quantitative susceptibility mapping through model-based deep image prior (MoDIP)

NeuroImage
The data-driven approach of supervised learning methods has limited applicability in solving dipole inversion in Quantitative Susceptibility Mapping (QSM) with varying scan parameters across different objects.
Zhuang Xiong, Yang Gao, Yin Liu, Amir Fazlollahi, Peter Nestor, Feng Liu, Hongfu Sun   +6 more
doaj   +1 more source