Analyzing the group sparsity based on the rank minimization methods
Sparse coding has achieved a great success in various image processing studies. However,
there is not any benchmark to measure the sparsity of image patch/group because sparse …
there is not any benchmark to measure the sparsity of image patch/group because sparse …
Non-convex weighted ℓp nuclear norm based ADMM framework for image restoration
Inspired by the fact that the matrix formed by nonlocal similar patches in a natural image is of
low rank, the nuclear norm minimization (NNM) has been widely used in various image …
low rank, the nuclear norm minimization (NNM) has been widely used in various image …
Compressed sensing image reconstruction via adaptive sparse nonlocal regularization
Compressed sensing (CS) has been successfully utilized by many computer vision
applications. However, the task of signal reconstruction is still challenging, especially when …
applications. However, the task of signal reconstruction is still challenging, especially when …
Multi-scale deep compressive imaging
Recently, deep learning-based compressive imaging (DCI) has surpassed conventional
compressive imaging in reconstruction quality and running speed. While multi-scale …
compressive imaging in reconstruction quality and running speed. While multi-scale …
Truncated nuclear norm minimization based group sparse representation for image restoration
T Geng, G Sun, Y Xu, J He - SIAM Journal on Imaging Sciences, 2018 - SIAM
Group sparse representation has shown great potential in image restoration, which can be
considered as a low-rank matrix approximation problem. The nuclear norm minimization …
considered as a low-rank matrix approximation problem. The nuclear norm minimization …
Group-based sparse representation for image compressive sensing reconstruction with non-convex regularization
Patch-based sparse representation modeling has shown great potential in image
compressive sensing (CS) reconstruction. However, this model usually suffers from some …
compressive sensing (CS) reconstruction. However, this model usually suffers from some …
Restricted structural random matrix for compressive sensing
Compressive sensing (CS) is well-known for its unique functionalities of sensing,
compressing, and security (ie equal importance of CS measurements). However, there is a …
compressing, and security (ie equal importance of CS measurements). However, there is a …
A fast multi-scale generative adversarial network for image compressed sensing
W Li, A Zhu, Y Xu, H Yin, G Hua - Entropy, 2022 - mdpi.com
Recently, deep neural network-based image compressed sensing methods have achieved
impressive success in reconstruction quality. However, these methods (1) have limitations in …
impressive success in reconstruction quality. However, these methods (1) have limitations in …
Multi-scale deep compressive sensing network
With joint learning of sampling and recovery, the deep learning-based compressive sensing
(DCS) has shown significant improvement in performance and running time reduction. Its …
(DCS) has shown significant improvement in performance and running time reduction. Its …
Analyzing the weighted nuclear norm minimization and nuclear norm minimization based on group sparse representation
Rank minimization methods have attracted considerable interest in various areas, such as
computer vision and machine learning. The most representative work is nuclear norm …
computer vision and machine learning. The most representative work is nuclear norm …