0.8% Nyquist computational ghost imaging via non-experimental deep learning
arXiv:2108.07673 · doi:10.1016/j.optcom.2022.128450
Abstract
We present a framework for computational ghost imaging based on deep learning and customized pink noise speckle patterns. The deep neural network in this work, which can learn the sensing model and enhance image reconstruction quality, is trained merely by simulation. To demonstrate the sub-Nyquist level in our work, the conventional computational ghost imaging results, reconstructed imaging results using white noise and pink noise via deep learning are compared under multiple sampling rates at different noise conditions. We show that the proposed scheme can provide high-quality images with a sampling rate of 0.8% even when the object is outside the training dataset, and it is robust to noisy environments. This method is excellent for various applications, particularly those that require a low sampling rate, fast reconstruction efficiency, or experience strong noise interference.
arXiv comment10 pages, 6 figures
References in corpus (13)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals
- Compressive ghost imaging
- Two-photon "ghost" imaging with thermal light
- Ghost imaging with a single detector
- Hadamard single-pixel imaging versus Fourier single-pixel imaging
- Super sub-Nyquist single-pixel imaging by means of cake-cutting Hadamard basis sort
- Lensless ghost imaging with true thermal light
- Computational ghost imaging using deep learning
- Compressive Object Tracking using Entangled Photons
- Anti-interference Computational Ghost Imaging with Pink Noise Speckle Patterns
- Sub-Nyquist computational ghost imaging with orthonormalized colored noise pattern
- Computational Ghost Imaging