A comparison study for image compression based on compressive sensing
Due to the limitation of the communication system resources, the compression is required to
reduce the complexity, the required storage, and the processing time. In traditional …
reduce the complexity, the required storage, and the processing time. In traditional …
Perceptual compressive sensing
J Du, X Xie, C Wang, G Shi - … Vision: First Chinese Conference, PRCV 2018 …, 2018 - Springer
Compressive sensing (CS) works to acquire measurements at sub-Nyquist rate and recover
the scene images. Existing CS methods always recover the scene images in pixel level. This …
the scene images. Existing CS methods always recover the scene images in pixel level. This …
Residual based compressed sensing recovery using sparse representations over a trained dictionary
A novel image compressed sensing (CS) reconstruction technique is proposed, wherein the
local sparsity and nonlocal similarities among the image patches are implicitly exploited to …
local sparsity and nonlocal similarities among the image patches are implicitly exploited to …
Efficient Minimization Algorithms for Compressive Sensing Based on Proximity Operator
This paper considers solving the unconstrained $\ell_q $-norm ($0\leq q< 1$) regularized
least squares ($\ell_q $-LS) problem for recovering sparse signals in compressive sensing …
least squares ($\ell_q $-LS) problem for recovering sparse signals in compressive sensing …
An improved algorithm of search for compressive sensing image recovery based on lp norm
J Yuan - 2015 Chinese Automation Congress (CAC), 2015 - ieeexplore.ieee.org
Compressed sensing theory by developing a signal sparse features, under the condition of
far less than the Nyquist sampling rate, the correct signal is acquired with random sampling …
far less than the Nyquist sampling rate, the correct signal is acquired with random sampling …
Smoothing Modified Newton Algorithm Based on LpNorm Regularization for Signal Recovery
Although the compressed sensing model has a good effect on signal recovery, it is quite
difficult to design reconstruction algorithms for compressive sensing. The challenge is that …
difficult to design reconstruction algorithms for compressive sensing. The challenge is that …
Image Compressive Sensing via Multiple Constraints
Y Fu, W Feng, W Peng - 2016 Eighth International Conference …, 2016 - ieeexplore.ieee.org
Total variation (TV) based method is widely used in image compressive sensing recently,
which also achieves great success in under-sampled recovery due to its virtue for edges …
which also achieves great success in under-sampled recovery due to its virtue for edges …
Image Compressed Sensing Reconstruction Based on Structural Group Total Variation
H ZHao, X Yang, J ZHang, C SUN, T ZHANG - 电子与信息学报, 2020 - jeit.ac.cn
To solve the problem that the traditional Compressed Sensing (CS) algorithm based on
Total Variation (TV) model can not effectively restore details and texture of image, which …
Total Variation (TV) model can not effectively restore details and texture of image, which …
Image compressed sensing recovery based on multi-scale group sparse representation
T Geng, G Sun, Y Xu, X Liu - 2018 25th International …, 2018 - ieeexplore.ieee.org
Compressed Sensing (CS) is intended to recover a high-dimensional but sparse vector by a
small number of linear sampling. Seeking an appropriate domain is of great importance to …
small number of linear sampling. Seeking an appropriate domain is of great importance to …
Iterative directional total variation refinement for compressive sensing image reconstruction
We propose a novel compressive sensing (CS) image reconstruction method based on
iterative directional total variation (TV) refinement. As is generally known, classical TV-based …
iterative directional total variation (TV) refinement. As is generally known, classical TV-based …