OpenICS: Open Image Compressive Sensing Toolbox and Benchmark

TL;DR

OpenICS is a comprehensive, standardized toolbox for image compressive sensing that facilitates benchmarking and comparison of various algorithms, aiming to advance research and practical applications in the field.

Contribution

It provides the first unified framework for multiple image compressive sensing algorithms along with a benchmarking study on their accuracy and efficiency.

Findings

Benchmarking results on reconstruction accuracy

Benchmarking results on reconstruction efficiency

OpenICS facilitates future research and development

Abstract

We present OpenICS, an image compressive sensing toolbox that includes multiple image compressive sensing and reconstruction algorithms proposed in the past decade. Due to the lack of standardization in the implementation and evaluation of the proposed algorithms, the application of image compressive sensing in the real-world is limited. We believe this toolbox is the first framework that provides a unified and standardized implementation of multiple image compressive sensing algorithms. In addition, we also conduct a benchmarking study on the methods included in this framework from two aspects: reconstruction accuracy and reconstruction efficiency. We wish this toolbox and benchmark can serve the growing research community of compressive sensing and the industry applying image compressive sensing to new problems as well as developing new methods more efficiently. Code and models are available at https://github.com/PSCLab-ASU/OpenICS. The project is still under maintenance, and we will keep this document updated.