Cargando…
An Entropy-Based Algorithm with Nonlocal Residual Learning for Image Compressive Sensing Recovery
Image recovery from compressive sensing (CS) measurement data, especially noisy data has always been challenging due to its implicit ill-posed nature, thus, to seek a domain where a signal can exhibit a high degree of sparsity and to design an effective algorithm have drawn increasingly more attenti...
Autores principales: | Xie, Zhonghua, Liu, Lingjun, Yang, Cui |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515429/ http://dx.doi.org/10.3390/e21090900 |
Ejemplares similares
-
Image Compressive Sensing via Hybrid Nonlocal Sparsity Regularization
por: Li, Lizhao, et al.
Publicado: (2020) -
Compressive Sensing via Nonlocal Smoothed Rank Function
por: Fan, Ya-Ru, et al.
Publicado: (2016) -
Parallel Computing of Patch-Based Nonlocal Operator and Its Application in Compressed Sensing MRI
por: Li, Qiyue, et al.
Publicado: (2014) -
Adaptive Compressive Sensing of Images Using Spatial Entropy
por: Li, Ran, et al.
Publicado: (2017) -
Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy
por: Jiang, Chundi, et al.
Publicado: (2018)