Deep architectures for image compression: a critical review
Deep learning architectures are now pervasive and filled almost all applications under
image processing, computer vision, and biometrics. The attractive property of feature …
image processing, computer vision, and biometrics. The attractive property of feature …
Learning-driven lossy image compression: A comprehensive survey
In the field of image processing and computer vision (CV), machine learning (ML)
architectures are widely used. Image compression problems can be solved using …
architectures are widely used. Image compression problems can be solved using …
Deep joint source-channel coding for wireless image transmission
E Bourtsoulatze, DB Kurka… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
We propose a joint source and channel coding (JSCC) technique for wireless image
transmission that does not rely on explicit codes for either compression or error correction; …
transmission that does not rely on explicit codes for either compression or error correction; …
[HTML][HTML] A novel lossy image compression algorithm using multi-models stacked AutoEncoders
S Fraihat, MA Al-Betar - Array, 2023 - Elsevier
The extensive use of images in many fields increased the demand for image compression
algorithms to overcome the transfer bandwidth and storage limitations. With image …
algorithms to overcome the transfer bandwidth and storage limitations. With image …
OSVFuseNet: Online Signature Verification by feature fusion and depth-wise separable convolution based deep learning
Abstract Online Signature Verification (OSV) techniques have been deployed in production
systems for decades, yet training the model for efficient classification of the test signature …
systems for decades, yet training the model for efficient classification of the test signature …
Attention-based convolutional autoencoders for 3d-variational data assimilation
We propose a new 'Bi-Reduced Space'approach to solving 3D Variational Data Assimilation
using Convolutional Autoencoders. We prove that our approach has the same solution as …
using Convolutional Autoencoders. We prove that our approach has the same solution as …
[PDF][PDF] Image compression using deep learning: methods and techniques
AS Abd-Alzhra, MSH Al-Tamimi - Iraqi Journal of Science, 2022 - iasj.net
In recent years images have been used widely by online social networks providers or
numerous organizations such as governments, police departments, colleges, universities …
numerous organizations such as governments, police departments, colleges, universities …
Encoder–decoder full residual deep networks for robust regression and spatiotemporal estimation
Although increasing hidden layers can improve the ability of a neural network in modeling
complex nonlinear relationships, deep layers may result in degradation of accuracy due to …
complex nonlinear relationships, deep layers may result in degradation of accuracy due to …
Deep residual autoencoder with multiscaling for semantic segmentation of land-use images
L Li - Remote Sensing, 2019 - mdpi.com
Semantic segmentation is a fundamental means of extracting information from remotely
sensed images at the pixel level. Deep learning has enabled considerable improvements in …
sensed images at the pixel level. Deep learning has enabled considerable improvements in …
The state of applying artificial intelligence to tissue imaging for cancer research and early detection
Artificial intelligence represents a new frontier in human medicine that could save more lives
and reduce the costs, thereby increasing accessibility. As a consequence, the rate of …
and reduce the costs, thereby increasing accessibility. As a consequence, the rate of …