Learning nonlocal sparse and low-rank models for image compressive sensing: Nonlocal sparse and low-rank modeling
The compressive sensing (CS) scheme exploits many fewer measurements than suggested
by the Nyquist–Shannon sampling theorem to accurately reconstruct images, which has …
by the Nyquist–Shannon sampling theorem to accurately reconstruct images, which has …
A tutorial on sparse signal reconstruction and its applications in signal processing
Sparse signals are characterized by a few nonzero coefficients in one of their transformation
domains. This was the main premise in designing signal compression algorithms …
domains. This was the main premise in designing signal compression algorithms …
Image restoration via reconciliation of group sparsity and low-rank models
Image nonlocal self-similarity (NSS) property has been widely exploited via various sparsity
models such as joint sparsity (JS) and group sparse coding (GSC). However, the existing …
models such as joint sparsity (JS) and group sparse coding (GSC). However, the existing …
Group sparsity residual constraint with non-local priors for image restoration
Group sparse representation (GSR) has made great strides in image restoration producing
superior performance, realized through employing a powerful mechanism to integrate the …
superior performance, realized through employing a powerful mechanism to integrate the …
Low-rankness guided group sparse representation for image restoration
As a spotlighted nonlocal image representation model, group sparse representation (GSR)
has demonstrated a great potential in diverse image restoration tasks. Most of the existing …
has demonstrated a great potential in diverse image restoration tasks. Most of the existing …
Triply complementary priors for image restoration
Recent works that utilized deep models have achieved superior results in various image
restoration (IR) applications. Such approach is typically supervised, which requires a corpus …
restoration (IR) applications. Such approach is typically supervised, which requires a corpus …
A benchmark for sparse coding: When group sparsity meets rank minimization
Sparse coding has achieved a great success in various image processing tasks. However, a
benchmark to measure the sparsity of image patch/group is missing since sparse coding is …
benchmark to measure the sparsity of image patch/group is missing since sparse coding is …
A hybrid structural sparsification error model for image restoration
Recent works on structural sparse representation (SSR), which exploit image nonlocal self-
similarity (NSS) prior by grouping similar patches for processing, have demonstrated …
similarity (NSS) prior by grouping similar patches for processing, have demonstrated …
Image restoration via joint low-rank and external nonlocal self-similarity prior
W Yuan, H Liu, L Liang, W Wang, D Liu - Signal Processing, 2024 - Elsevier
Recent studies have revealed that joint priors, such as joint sparsity and external nonlocal
self-similarity (ENSS) prior and joint low-rank and sparsity prior, are extremely effective in …
self-similarity (ENSS) prior and joint low-rank and sparsity prior, are extremely effective in …
Secure and traceable image transmission scheme based on semitensor product compressed sensing in telemedicine system
With the rapid development of the Internet of Things technology and the gradual upgrade of
communication methods, a new type of telemedicine system encounters a golden …
communication methods, a new type of telemedicine system encounters a golden …