Computational ghost imaging with hybrid transforms by integrating Hadamard, discrete cosine, and Haar matrices

•Hybrid transforms are integrated in computational ghost imaging to simplify image reconstruction.•In experiments, hybridizations of Hadamard, discrete cosine, and Haar transform matrices are exploited.•With hybrid transforms, computational ghost imaging makes image encoding and image compression conveniently.•Computational ghost imaging with hybrid transforms may find flexible applications in image processing. 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 with the corresponding inverse matrices. In experiment, six sets of transform hybridizations 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, showing 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 in one experiment. Ghost imaging with hybrid transforms may find flexible applications in image processing, such as image compression and image encryption.