arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2103.00652cs.CVcs.LGeess.IV

OpenICS: Open Image Compressive Sensing Toolbox and Benchmark

Arizona State University(亚利桑那州立大学)

Jonathan Zhao, Matthew Westerham, Mark Lakatos-Toth, Zhikang Zhang, Avi Moskoff, Fengbo Ren

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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