# Compressive Sensing

75 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

# Greatest papers with code

# Provable Dynamic Robust PCA or Robust Subspace Tracking

Dynamic robust PCA refers to the dynamic (time-varying) extension of robust PCA (RPCA).

# One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models

On the other hand, traditional methods using signal priors can be used in all linear inverse problems but often have worse performance on challenging tasks.

# ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing

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.

# 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.

# Deep Fully-Connected Networks for Video Compressive Sensing

In this work we present a deep learning framework for video compressive sensing.

# 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?

# Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds

In this work, we study unfolded ISTA (Iterative Shrinkage Thresholding Algorithm) for sparse signal recovery.

# One Network to Solve Them All -- Solving Linear Inverse Problems Using Deep Projection Models

While deep learning methods have achieved state-of-the-art performance in many challenging inverse problems like image inpainting and super-resolution, they invariably involve problem-specific training of the networks.

# SNIPS: Solving Noisy Inverse Problems Stochastically

In this work we introduce a novel stochastic algorithm dubbed SNIPS, which draws samples from the posterior distribution of any linear inverse problem, where the observation is assumed to be contaminated by additive white Gaussian noise.

# A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning

In recent, nonconvex regularization based sparse and low-rank recovery is of considerable interest and it in fact is a main driver of the recent progress in nonconvex and nonsmooth optimization.