Image compression using compressed sensing.
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.
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.