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

TL;DR

This paper introduces a hybrid transform approach for computational ghost imaging by integrating Hadamard, discrete cosine, and Haar matrices, enabling flexible image reconstruction and compression.

Contribution

It proposes a novel hybrid transform method combining multiple matrices for ghost imaging, enhancing image compression and reconstruction flexibility.

Findings

Hybrid transform sets successfully reconstruct images with reduced measurements.

Sub-Nyquist sampling is feasible in the hybrid transform framework.

The method extends to more transforms for advanced applications.

Abstract

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.