Compressive Sensing
109 papers with code • 5 benchmarks • 4 datasets
Compressive Sensing is a new signal processing framework for efficiently acquiring and reconstructing a signal that have a sparse representation in a fixed linear basis.
Source: Sparse Estimation with Generalized Beta Mixture and the Horseshoe Prior
Benchmarks
These leaderboards are used to track progress in Compressive Sensing
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Libraries
Use these libraries to find Compressive Sensing models and implementations
BSD
Urban100
Set5
Set11
Most implemented papers
Deep Fully-Connected Networks for Video Compressive Sensing
In this work we present a deep learning framework for video compressive sensing.
DeepBinaryMask: Learning a Binary Mask for Video Compressive Sensing
In this paper, we propose a novel encoder-decoder neural network model referred to as DeepBinaryMask for video compressive sensing.
An Efficient Method for Robust Projection Matrix Design
Without requiring of training data, we can efficiently design the robust projection matrix and apply it for most of CS systems, like a CS system for image processing with a conventional wavelet dictionary in which the SRE matrix is generally not available.
Interpretable Recurrent Neural Networks Using Sequential Sparse Recovery
Recurrent neural networks (RNNs) are powerful and effective for processing sequential data.
Online Learning Sensing Matrix and Sparsifying Dictionary Simultaneously for Compressive Sensing
The simulation results on natural images demonstrate the effectiveness of the suggested online algorithm compared with the existing methods.
Learning to Invert: Signal Recovery via Deep Convolutional Networks
y0umu/DeepInverse-Reimplementation
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The promise of compressive sensing (CS) has been offset by two significant challenges.
DR2-Net: Deep Residual Reconstruction Network for Image Compressive Sensing
Accordingly, DR$^{2}$-Net consists of two components, \emph{i. e.,} linear mapping network and residual network, respectively.
Sparse Depth Sensing for Resource-Constrained Robots
We address the following question: is it possible to reconstruct the geometry of an unknown environment using sparse and incomplete depth measurements?
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing
jianzhangcs/ISTA-Net-PyTorch
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CVPR 2018
With the aim of developing a fast yet accurate algorithm for compressive sensing (CS) reconstruction of natural images, we combine in this paper the merits of two existing categories of CS methods: the structure insights of traditional optimization-based methods and the speed of recent network-based ones.
Adaptive Measurement Network for CS Image Reconstruction
Chinmayrane16/ReconNet-PyTorch
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In this paper, we propose an adaptive measurement network in which measurement is obtained by learning.
