The reconstruction of Compressed Sensing is iteration-based and contains numerous divisions,
thereby costing tremendous processing time.
To eliminate divisions, we adopt a sparse sensing matrix consisting mainly of zero-vectors.
After removing zero-vectors, an invertible full-rank matrix is obtained.
This allows the iteration-based reconstruction procedure to be replaced by a single matrix multiplication.
The proposed architecture is verified on the Xilinx Artix-7 FPGA.
The result shows that our work accelerates the state-of-the-art method by 65 × and achieves 81.92Gpixels/s
reconstruction.
Top: The architecture of PGTformer.
Top: The architecture of PGTformer.
Quantitative comparison on VFHQ Blind setting.
@INPROCEEDINGS{9937930,
author={Xu, Jiayao and Chi, Pham Do Kim and Fu, Chen and Zhou, Jinjia},
booktitle={2022 IEEE International Symposium on Circuits and Systems (ISCAS)},
title={An 81.92Gpixels/s Fast Reconstruction of Images from Compressively Sensed Measurements},
year={2022},
volume={},
number={},
pages={2978-2982},
keywords={Costs;Reconstruction algorithms;Hardware;Matrices;Time measurement;Sensors;Sparse matrices;Compressed Sensing;Reconstruction Algorithm;Real-time Reconstruction;Hardware implementation},
doi={10.1109/ISCAS48785.2022.9937930}}