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.
翻译:我们提出OpenICS,这是一个图像压缩感测工具箱,其中包括过去十年中提议的多种图像压缩感测和重建算法;由于实施和评估拟议算法缺乏标准化,在现实世界中应用图像压缩感测是有限的;我们认为,这个工具箱是第一个统一和标准化实施多种图像压缩感测算法的框架;此外,我们还从两个方面,即重建准确性和重建效率,对这一框架所包含的方法进行基准研究;我们希望这个工具箱和基准能够服务于不断增长的压缩感测研究界和对新问题应用图像压缩感测的产业,并更有效地开发新方法;守则和模型可在https://github.com/PSCLAB-ASU/OpenICS查阅。该项目仍在维护中,我们将不断更新该文件。