Adaptive Selection of Sampling-Reconstruction in Fourier Compressed Sensing
<|reference_start|>Adaptive Selection of Sampling-Reconstruction in Fourier Compressed Sensing: Compressed sensing (CS) has emerged to overcome the inefficiency of Nyquist sampling. However, traditional optimization-based reconstruction is slow and can not yield an exact image in practice. Deep learning-based reconstruction has been a promising alternative to optimization-based reconstruction, outperforming it in accuracy and computation speed. Finding an efficient sampling method with deep learning-based reconstruction, especially for Fourier CS remains a challenge. Existing joint optimization of sampling-reconstruction works ($\mathcal{H}_1$) optimize the sampling mask but have low potential as it is not adaptive to each data point. Adaptive sampling ($\mathcal{H}_2$) has also disadvantages of difficult optimization and Pareto sub-optimality. Here, we propose a novel adaptive selection of sampling-reconstruction ($\mathcal{H}_{1.5}$) framework that selects the best sampling mask and reconstruction network for each input data. We provide theorems that our method has a higher potential than $\mathcal{H}_1$ and effectively solves the Pareto sub-optimality problem in sampling-reconstruction by using separate reconstruction networks for different sampling masks. To select the best sampling mask, we propose to quantify the high-frequency Bayesian uncertainty of the input, using a super-resolution space generation model. Our method outperforms joint optimization of sampling-reconstruction ($\mathcal{H}_1$) and adaptive sampling ($\mathcal{H}_2$) by achieving significant improvements on several Fourier CS problems.<|reference_end|>
@article{hong2024adaptive,
    title={Adaptive Selection of Sampling-Reconstruction in Fourier Compressed
  Sensing},
    author={Seongmin Hong, Jaehyeok Bae, Jongho Lee, Se Young Chun},
    journal={arXiv preprint arXiv:2409.11738},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11738},
    primaryClass={eess.IV cs.CV}
}