compressive sensing
Quantized Iterative Hard Thresholding: Bridging 1-bit and High Resolution Quantized Compressed Sensing
Abstract: In this work, we show that reconstructing a sparse signal from quantized compressive measurement can be achieved in an unified formalism whatever the (scalar) quantization resolution, i.e., from 1-bit to high resolution assumption.
What can we learn from the Compressed Sensing theory?
Abstract: The recent theory of Compressed Sensing (CS) induces a revolution in the design of signal sensors and of imaging devices. By the advent of increased computing capabilities, along with recent theoretical and numerical breakthroughs in the fields of Image Processing, Sparse Signal Representations, Inverse Problem solving and Convex Optimization, the term Sensing is no more a synonym for readily rendering human readable signals.
Robust 1-Bit Compressive Sensing: How the Sign of Random Projections Distinguishes Sparse Vectors
Joint work with J. Laska, P. Boufounos, R. Baraniuk
Abstract: The Compressive Sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by reducing the sampling rate required to acquire and stably recover sparse signals.
Robust 1-Bit Compressive Sensing: How the Sign of Random Projections Distinguishes Sparse Vectors
Joint work with J. Laska, P. Boufounos, and R. Baraniuk.
New class of RIP matrices?
Wow, almost one year and half without any post here… Shame on me! I’ll try to be more productive with shorter posts now ;-)
I just found this interesting paper about concentration properties of submodular function (very common in “Graph Cut” methods for instance) on arxiv:
