  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.
