Sparse decomposition is one of the core issue of compressive sensing ghost image. At this stage, traditional
methods still have the problems of poor sparsity and low reconstruction accuracy, such as discrete fourier transform and
discrete cosine transform. In order to solve these problems, joint orthogonal bases transform is proposed to optimize
ghost imaging. First, introduce the principle of compressive sensing ghost imaging and point out that sparsity is related
to the minimum sample data required for imaging. Then, analyze the development and principle of joint orthogonal
bases in detail and find out it can use less nonzero coefficients to reach the same identification effect as other methods.
So, joint orthogonal bases transform is able to provide the sparsest representation. Finally, the experimental
setup is built in order to verify simulation results. Experimental results indicate that the PSNR of joint orthogonal bases
is much higher than traditional methods by using same sample data in compressive sensing ghost image.Therefore, joint
orthogonal bases transform can realize better imaging quality under less sample data, which can satisfy the system
requirements of convenience and rapid speed in ghost image.