# compressed_sensing

**Repository Path**: ethan996/compressed_sensing

## Basic Information

- **Project Name**: compressed_sensing
- **Description**: Image compression using compressed sensing.
- **Primary Language**: Unknown
- **License**: GPL-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-05-11
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# compressed_sensing
[![License](https://img.shields.io/badge/license-GPLv2-blue.svg)](LICENSE)

Image compression using compressed sensing.

## Summary
This repository is under development as part of a class project for UC Berkeley's EE227BT Convex Optimization course. The authors are David Fridovich-Keil and Grace Kuo, both graduate students in the EECS department at UC Berkeley.

## Organization
The files in this repository are organized as follows.

The `compressed_sensing/presentation` directory contains a copy of our slide deck, and also several images used in the slides.

The `compressed_sensing/writeup` directory contains a copy of our final report.

The `compressed_sensing/data` directory contains three example images. Virtually all of our examples in the slides and the report use the `lenna.png` image.

The `compressed_sensing/reconstructions` directory contains two sub-directories, `matlab figures` and `python figures`, which (not suprisingly) contain compression and reconstruction results created by test scripts written in MATLAB and Python, respectively.

The `compressed_sensing/src` directory also contains two sub-directories. The `matlab` sub-directory contains our most up-to-date code base; these are the functions and scripts we use to generate all the figures in our presentation and report. The `python` sub-directory contains an earlier version of the code base.