Block Compressed Sensing of Images Using Directional Transforms
By Sungkwang Mun, James E. Fowler. Data Compression Conference, 2010
558 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Computational Mechanics, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/954c689c10d092bbdcaabb4e5788b90bdb7184d5
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
- Christopher A. Metzler
- Jian Zhang
- Richard Baraniuk
- Feng Jiang
- Debin Zhao
- Shaohui Liu
- Wuzhen Shi
- Bernard Ghanem
In Computational Mechanics
- Bernard Ghanem
- Christopher A. Metzler
- Richard Baraniuk
- Jian Zhang
- Debin Zhao
- Shaohui Liu
- Feng Jiang
- Wuzhen Shi
In Computer Vision and Pattern Recognition
- Bernard Ghanem
- Jian Zhang
- Richard Baraniuk
- Christopher A. Metzler
- Shaohui Liu
- Debin Zhao
- Feng Jiang
- Wuzhen Shi
Citations in the corpus
- Image Compressed Sensing Using Convolutional Neural Network (2020, methodology): “1) Initial Reconstruction Network: Given the CS measurements, BCS usually uses a pseudo-inverse matrix to obtain the initial reconstructed image [24], [25].”
- From Denoising to Compressed Sensing (2014, background): “Note that in the development of D-AMP we are not concerned about whether the algorithm is approximating a posterior distribution for a certain prior or not.”
- ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing (2017, methodology): “DCT, wavelet [31], and gradient domain [22]) usually result in poor reconstruction performance.”