Sub-Nyquist computational ghost imaging with orthonormalized colored noise pattern

Abstract

Computational ghost imaging generally requires a large number of pattern illumination to obtain a high-quality image. The colored noise speckle pattern was recently proposed to substitute the white noise pattern in a variety of noisy environments and gave a significant signal-to-noise ratio enhancement even with a limited number of patterns. We propose and experimentally demonstrate here an orthonormalization approach based on the colored noise patterns to achieve sub-Nyquist computational ghost imaging. We tested the reconstructed image in quality indicators such as the contrast-to-noise ratio, the mean square error, the peak signal to noise ratio, and the correlation coefficient. The results suggest that our method can provide high-quality images while using a sampling ratio an order lower than the conventional methods.

7 pages, 7 figures

References in corpus (3)

• Hadamard single-pixel imaging versus Fourier single-pixel imaging
• Anti-interference Computational Ghost Imaging with Pink Noise Speckle Patterns
• Computational Ghost Imaging

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