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OpenICS: Open Image Compressive Sensing Toolbox and Benchmark

arXiv 2103.00652 ↗First posted 28 Feb 2021cs.CV cs.LG eess.IVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 26 references that are in the map. First posted on arXiv 28 Feb 2021.

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11 of 26 references.

References · 11

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
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  4. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  5. 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
  6. 2017Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolutioncited by 452
  7. 2017Compressed Sensing using Generative Modelscited by 337
  8. 2014From Denoising to Compressed Sensingcited by 67
  9. 2017Learned D-AMP: Principled Neural Network based Compressive Image Recoverycited by 65
  10. 2019Deep Compressed Sensingcited by 46
  11. 2018LAPRAN: A Scalable Laplacian Pyramid Reconstructive Adversarial Network for Flexible Compressive Sensing Reconstructioncited by 18

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