Computational compressive imaging reconstruction with a simulated-annealing-based Ising solver
Computational compressive imaging, such as single-pixel imaging and lensless imaging, is a promising imaging technique, enabling the reconstruction of objects from under-sampled measurements through compressed sensing algorithms or deep learning methods. However, the demand for electronic computational resources grows rapidly when recovering high-resolution and multi-dimensional images and training neural networks with numerous parameters, which has become a bottleneck for the widespread adoption of this technique. Here, we propose a physics-driven reconstruction framework that formulates imaging inverse problems as Ising models and directly recovers target objects through the self-evolut...
| Authors | Ze Zheng, Xiaoyan Wu, Hang Xu et al. |
|---|---|
| Primary category | physics.optics |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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