Computational holography is one of the most potential 3D display technologies. In recent years, with the continuous iterative evolution of holographic optical theory and the continuous maturity of computer software and hardware technologies, related technologies have developed rapidly. Correlation optimization algorithm is a research hotspot at present. This paper studies and analyzes several pure phase hologram generation and optimization algorithms commonly used in this field in recent years, and points out that the above algorithms have the limitation that the quality of reconstructed images cannot be further effectively improved; for this problem, we propose a new optimization idea based on high-dimensional space is to find the reconstructed image with similar pixel distribution to the target image in the highdimensional space. At the same time, we propose a more practical pixel correction method, which considers the reconstruction of the image from the high-dimensional space. Optimization can further effectively reduce interference such as speckle noise and improve the quality of reconstructed images. Through experimental calculation, we obtained the peak signal-to-noise ratio and structural similarity of the reconstructed image before and after the correction of the common algorithm and the original image and compared them. It can be verified through experiments that the numerical improvement of the above two indicators obtained by our proposed method is obvious. The quality of the reconstructed images has also been better improved.
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