OpenICS: Open Image Compressive Sensing Toolbox and Benchmark
This work provides a standardized framework for image compressive sensing, which is incremental as it consolidates existing methods into a unified toolbox.
The authors introduced OpenICS, a toolbox and benchmark for image compressive sensing that standardizes implementations of multiple algorithms from the past decade, addressing the lack of standardization that limits real-world applications. They conducted benchmarking on reconstruction accuracy and efficiency to support the research community and industry.
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.