  A scenario of ghost imaging with hybrid transform approach is proposed by
integrating Hadamard, discrete cosine, and Haar matrices. The measurement
matrix is formed by the Kronecker product of the two different transform
matrices. The image information can be conveniently reconstructed by the
corresponding inverse matrices. In experiment, six hybridization sets are
performed in computational ghost imaging. For an object of staggered stripes,
only one bucket signal survives in the Hadamard-cosine, Haar-Hadamard, and
Haar-cosine hybridization sets, demonstrating flexible image compression. For a
handmade windmill object, the quality factors of the reconstructed images vary
with the hybridization sets. Sub-Nyquist sampling can be applied to either or
both of the different transform matrices in each hybridization set in
experiment. The hybridization method can be extended to apply more transforms
at once. Ghost imaging with hybrid transforms may find flexible applications in
image processing, such as image compression and image encryption.
