Block Compressed Sensing of Natural Images

By Lu Gan. International Conference on Digital Signal Processing, 2007

790 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Image Compressed Sensing Using Convolutional Neural Network (2020, methodology): “In [11], Gan proposed”
From Denoising to Compressed Sensing (2014, methodology): “2) Test Settings: ALSB uses rows drawn from a 322 × 322 orthonormalized Gaussian measurement matrix to perform block-based compressed sensing, as described in [57].”
Kronecker Compressive Sensing (2012, background): “Similarly, partitioned measurements have been proposed for CS of low-dimensional signals for computational purposes [25], [26], [41].”
Toward real-time terahertz imaging (2018, background): “[374], is to divide the original object into several regions and to simultaneously apply the same binary operator in each region.”
ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Measurements (2016, neutral): “Contributions: a) We propose a non-iterative and extremely fast reconstruction algorithm for block CS imaging [12].”
Block Compressed Sensing of Images Using Directional Transforms (2010, methodology): “Using BCS rather than random sampling applied to the entire image x has several merits [7].”