Results 61 to 70 of about 1,132,388 (176)

Deep Image Super Resolution via Natural Image Priors [PDF]

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
Single image super-resolution (SR) via deep learning has recently gained significant attention in the literature. Convolutional neural networks (CNNs) are typically learned to represent the mapping between low-resolution (LR) and high-resolution (HR) images/patches with the help of training examples.
Mousavi, Hojjat S., Guo, Tiantong, Monga, Vishal   +2 more
openaire   +2 more sources

Practical phase retrieval using double deep image priors

Electronic Imaging, 2023
Phase retrieval (PR) concerns the recovery of complex phases from complex magnitudes. We identify the connection between the difficulty level and the number and variety of symmetries in PR problems. We focus on the most difficult far-field PR (FFPR), and propose a novel method using double deep image priors.
Zhuang, Zhong, Yang, David, Hofmann, Felix, Barmherzig, David, Sun, Ju   +4 more
openaire   +2 more sources

Learning Spectral–Spatial-Former Deep Prior for Hyperspectral Image Superresolution

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The superresolution (SR) technique is a leading solution for achieving high spatial–spectral resolution in hyperspectral (HS) images, which current sensors struggle to provide due to cost and physical constraints.
Zeinab Dehghan, Jingxiang Yang, Abdolraheem Khader, Jian Fang, Liang Xiao   +4 more
doaj   +1 more source

MemNet: A Persistent Memory Network for Image Restoration

, 2017
Recently, very deep convolutional neural networks (CNNs) have been attracting considerable attention in image restoration. However, as the depth grows, the long-term dependency problem is rarely realized for these very deep models, which results in the ...
Liu, Xiaoming, Tai, Ying, Xu, Chunyan, Yang, Jian   +3 more
core   +1 more source

Unsupervised noisy image segmentation using Deep Image Prior

Mathematics and Computers in Simulation
The so called Deep Image Prior paradigm stands as an exceptional advancement at the intersection of inverse problems and deep learning. By leveraging the inherent regularization properties of deep networks, Deep Image Prior has recently emerged as a landmark approach in addressing various imaging problems, including denoising, JPEG artifacts removal ...
Alessandro Benfenati, Ambra Catozzi, Giorgia Franchini, Federica Porta   +3 more
openaire   +1 more source

From shallow sea to deep sea: research progress in underwater image restoration

Frontiers in Marine Science, 2023
Underwater images play a crucial role in various fields, including oceanographic engineering, marine exploitation, and marine environmental protection.
Wei Song, Yaling Liu, Dongmei Huang, Bing Zhang, Zhihao Shen, Huifang Xu   +5 more
doaj   +1 more source

"Zero-Shot" Super-Resolution using Deep Internal Learning

, 2017
Deep Learning has led to a dramatic leap in Super-Resolution (SR) performance in the past few years. However, being supervised, these SR methods are restricted to specific training data, where the acquisition of the low-resolution (LR) images from their ...
Cohen, Nadav, Irani, Michal, Shocher, Assaf   +2 more
core   +1 more source

Image Deconvolution with Deep Image and Kernel Priors [PDF]

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019
Image deconvolution is the process of recovering convolutional degraded images, which is always a hard inverse problem because of its mathematically ill-posed property. On the success of the recently proposed deep image prior (DIP), we build an image deconvolution model with deep image and kernel priors (DIKP).
Wang, Zhunxuan, Wang, Zipei, Li, Qiqi, Bilen, Hakan   +3 more
openaire   +2 more sources

Removal of speckle noises from ultrasound images using five different deep learning networks

Engineering Science and Technology, an International Journal, 2022
Image enhancement methods are applied to medical images to reduce the noise that they contain. There are many academic studies in the literature using classical image enhancement methods.
Onur Karaoğlu, Hasan Şakir Bilge, İhsan Uluer   +2 more
doaj   +1 more source

Hierarchy-based Image Embeddings for Semantic Image Retrieval

, 2019
Deep neural networks trained for classification have been found to learn powerful image representations, which are also often used for other tasks such as comparing images w.r.t. their visual similarity. However, visual similarity does not imply semantic
Barz, Björn, Denzler, Joachim
core   +1 more source