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Practical Compact Deep Compressed Sensing
Summary
This study introduces PCNet, a deep learning network for compressed sensing (CS) that significantly reduces sampling costs for image reconstruction. PCNet demonstrates superior accuracy and generalization, especially for high-resolution images.
Area of Science:
- Computer Vision
- Signal Processing
- Machine Learning
Background:
- Deep networks have shown success in compressed sensing (CS), reducing sampling costs.
- CS enables significant reductions in data acquisition expenses.
- Growing attention is given to CS for its efficiency in various applications.
Purpose of the Study:
- Propose PCNet, a practical and compact deep network for general image CS.
- Design a novel collaborative sampling operator for efficient data acquisition.
- Develop an enhanced reconstruction network for improved performance.
Main Methods:
- PCNet employs a collaborative sampling operator with deep conditional filtering and dual-branch fast sampling.
- The sampling operator utilizes learned convolutions and transforms like DCT with Gaussian matrices.
- An enhanced proximal gradient descent unrolled network facilitates image reconstruction.
Main Results:
- PCNet achieves superior reconstruction accuracy and generalization across natural, quantized, and self-supervised CS.
- The network performs exceptionally well on high-resolution images.
- A deployment-oriented scheme enables hardware integration for single-pixel CS systems.
Conclusions:
- PCNet offers flexibility, interpretability, and strong recovery performance for arbitrary sampling rates.
- The proposed methods advance the field of deep learning for compressed sensing.
- PCNet provides a practical solution for efficient image acquisition and reconstruction.
