Results 41 to 50 of about 1,132,388 (176)

Hyperspectral Mixed Noise Removal via Spatial-Spectral Constrained Unsupervised Deep Image Prior

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Recently, deep learning-based methods are proposed for hyperspectral images (HSIs) denoising. Among them, unsupervised methods such as deep image prior (DIP)-based methods have received much attention because these methods do not require any training ...
Yi-Si Luo, Xi-Le Zhao, Tai-Xiang Jiang, Yu-Bang Zheng, Yi Chang   +4 more
doaj   +1 more source

A Synergistic Approach to Image Restoration DenseNet Enhanced Deep Image Prior [PDF]

E3S Web of Conferences
The DenseNet-Enhanced Deep Image Prior (Dense-DIP) model employs a combination of theories from DenseNet architecture and Deep Image Prior framework to achieve the best results in terms of image restoration.
A Senthil Anandhi., Jaiganesh M.
doaj   +1 more source

Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal [PDF]

, 2015
In this paper, we address the problem of estimating and removing non-uniform motion blur from a single blurry image. We propose a deep learning approach to predicting the probabilistic distribution of motion blur at the patch level using a convolutional ...
Cao, Wenfei, Ponce, Jean, Sun, Jian, Xu, Zongben   +3 more
core   +5 more sources

Deep Mr Image Super-Resolution Using Structural Priors [PDF]

2018 25th IEEE International Conference on Image Processing (ICIP), 2018
Accepted to IEEE ICIP ...
Cherukuri, Venkateswararao, Guo, Tiantong, Schiff, Steven J., Monga, Vishal   +3 more
openaire   +3 more sources

Deep Highlight Removal Using Temporal Dark Prior in High-Speed Domain

IEEE Access, 2023
This paper proposes a deep highlight removal method based on the dichromatic model under alternating current (AC) light sources with a new diffuse prior on temporal domain, temporal dark prior.
Jeong-Won Ha, Kang-Kyu Lee, Jun-Sang Yoo, Jong-Ok Kim   +3 more
doaj   +1 more source

Low-Dose CT Image Reconstruction With a Deep Learning Prior

IEEE Access, 2020
In low-dose computed tomography (LDCT), a penalized weighted least squares (PWLS) approach that incorporates the Poisson statistics of X-ray photons can significantly reduce excessive quantum noise.
Hyoung Suk Park, Kyungsang Kim, Kiwan Jeon   +2 more
doaj   +1 more source

Learning Deep CNN Denoiser Prior for Image Restoration

, 2017
Model-based optimization methods and discriminative learning methods have been the two dominant strategies for solving various inverse problems in low-level vision.
Gu, Shuhang, Zhang, Kai, Zhang, Lei, Zuo, Wangmeng   +3 more
core   +1 more source

DIP-UP: Deep Image Prior for Unwrapping Phase

Information
Phase images from gradient echo MRI sequences reflect underlying magnetic field inhomogeneities but are inherently wrapped within the range of −π to π, requiring phase unwrapping to recover the true phase.
Xuanyu Zhu, Yang Gao, Zhuang Xiong, Wei Jiang, Feng Liu, Hongfu Sun   +5 more
doaj   +1 more source

Deep Networks for Image Super-Resolution with Sparse Prior

, 2015
Deep learning techniques have been successfully applied in many areas of computer vision, including low-level image restoration problems. For image super-resolution, several models based on deep neural networks have been recently proposed and attained ...
Han, Wei, Huang, Thomas, Liu, Ding, Wang, Zhaowen, Yang, Jianchao   +4 more
core   +1 more source

Deep Image Matting

, 2017
Image matting is a fundamental computer vision problem and has many applications. Previous algorithms have poor performance when an image has similar foreground and background colors or complicated textures. The main reasons are prior methods 1) only use
Cohen, Scott, Huang, Thomas, Price, Brian, Xu, Ning   +3 more
core   +1 more source