Deep Learning of Compressed Sensing Operators with Structural Similarity Loss

06/25/2019
by   Yochai Zur, et al.
0

Compressed sensing (CS) is a signal processing framework for efficiently reconstructing a signal from a small number of measurements, obtained by linear projections of the signal. In this paper we present an end-to-end deep learning approach for CS, in which a fully-connected network performs both the linear sensing and non-linear reconstruction stages. During the training phase, the sensing matrix and the non-linear reconstruction operator are jointly optimized using Structural similarity index (SSIM) as loss rather than the standard Mean Squared Error (MSE) loss. We compare the proposed approach with state-of-the-art in terms of reconstruction quality under both losses, i.e. SSIM score and MSE score.

READ FULL TEXT
research
06/05/2016

A Deep Learning Approach to Block-based Compressed Sensing of Images

Compressed sensing (CS) is a signal processing framework for efficiently...
research
10/30/2016

Compressed Learning: A Deep Neural Network Approach

Compressed Learning (CL) is a joint signal processing and machine learni...
research
06/28/2018

Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction

Deep learning approaches have shown promising performance for compressed...
research
10/04/2013

A Novel Progressive Image Scanning and Reconstruction Scheme based on Compressed Sensing and Linear Prediction

Compressed sensing (CS) is an innovative technique allowing to represent...
research
11/22/2019

Compressed Sensing Channel Estimation for OFDM with non-Gaussian Multipath Gains

This paper analyzes the impact of non-Gaussian multipath component (MPC)...
research
12/17/2013

Recursive Compressed Sensing

We introduce a recursive algorithm for performing compressed sensing on ...
research
03/28/2021

TULVCAN: Terahertz Ultra-broadband Learning Vehicular Channel-Aware Networking

Due to spectrum scarcity and increasing wireless capacity demands, terah...

Please sign up or login with your details

Forgot password? Click here to reset