Deep sparse representation based image restoration with denoising prior
W Xu, Q Zhu, N Qi, D Chen - … on Circuits and Systems for Video …, 2022 - ieeexplore.ieee.org
As a powerful statistical signal modeling technique, sparse representation has been widely
used in various image restoration (IR) applications. The sparsity-based methods have …
used in various image restoration (IR) applications. The sparsity-based methods have …
Robust correlation filter learning with continuously weighted dynamic response for UAV visual tracking
Unmanned aerial vehicles (UAVs) visual tracking has always been a challenging task.
Existing correlation filter tracking algorithms typically utilize the histograms of oriented …
Existing correlation filter tracking algorithms typically utilize the histograms of oriented …
DeGAN: Mixed noise removal via generative adversarial networks
Q Lyu, M Guo, Z Pei - Applied Soft Computing, 2020 - Elsevier
Restoration of images corrupted by mixed noise (eg, additive white Gaussian noise and
impulse noise) is very difficult due to the complexity of the mixed noise distribution. Various …
impulse noise) is very difficult due to the complexity of the mixed noise distribution. Various …
Deep unfolding network for efficient mixed video noise removal
Existing image and video denoising algorithms have focused on removing homogeneous
Gaussian noise. However, this assumption with noise modeling is often too simplistic for the …
Gaussian noise. However, this assumption with noise modeling is often too simplistic for the …
Hyperspectral image classification with multi-scale feature extraction
Spectral features cannot effectively reflect the differences among the ground objects and
distinguish their boundaries in hyperspectral image (HSI) classification. Multi-scale feature …
distinguish their boundaries in hyperspectral image (HSI) classification. Multi-scale feature …
Selecting post-processing schemes for accurate detection of small objects in low-resolution wide-area aerial imagery
In low-resolution wide-area aerial imagery, object detection algorithms are categorized as
feature extraction and machine learning approaches, where the former often requires a post …
feature extraction and machine learning approaches, where the former often requires a post …
Low-rank enforced fault feature extraction of rolling bearings in a complex noisy environment: A perspective of statistical modeling of noises
R Wang, H Fang, Y Zhang, L Yu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The fault feature extraction of rolling element bearings is of critical interest for fault diagnosis.
The fault impulses are always buried in strong and complex background noise, which makes …
The fault impulses are always buried in strong and complex background noise, which makes …
A Novel Truncated Norm Regularization Method for Multi-channel Color Image Denoising
Due to the high flexibility and remarkable performance, low-rank approximation has been
widely studied for color image denoising. However, existing methods usually ignore the …
widely studied for color image denoising. However, existing methods usually ignore the …
Iterative relaxed collaborative representation with adaptive weights learning for noise robust face hallucination
In recent years, the collaborative representation (CR)-based techniques have been widely
employed for face hallucination. However, the conventional CR model becomes less …
employed for face hallucination. However, the conventional CR model becomes less …
An adaptive global–local interactive non-local boosting network for mixed noise removal
Mixed noise removal is a challenging task for image interpretation due to the complexity of
mixed noise distribution. Benefiting from the superior multiple nonlinear transformations …
mixed noise distribution. Benefiting from the superior multiple nonlinear transformations …