Non-iterative phase hologram generation with adaptive weighted constraints for color holographic display

Abstract

To provide better reconstruction quality, it takes more time for iterative algorithms, especially when multiple holograms need to be computed. A non-iterative method for calculating a phase hologram with adaptive weighted constraints is proposed, which iteratively calculates the optimized phase with a rectangular aperture as the amplitude and multiplies the initial quadratic phase as the initial complex amplitude. Subtraction feedback is introduced to improve the visual effect and avoid the overcompensation problem of division feedback. The proposed method is suitable for generating non-iterative phase holograms with different sizes, and it can further be applied to the calculation of color holograms. It improves computational speed under the premise of ensuring reconstruction quality and can adapt to the needs of different systems with different sizes or different location requirements for holographic reconstruction. Optical experiments also demonstrate the effectiveness of this method.

Research on multiple-image encryption method using modified Gerchberg–Saxton algorithm and chaotic systems

“…POHs are mainstream computer-generated holograms 31 , 32 because they have no conjugate diffracted light and high energy utilization. In this paper, a modified GS algorithm with a bandwidth constraint 33 is used.…”

Iterative phase hologram generation with adaptive weighted constraints for color holographic display

“…In the first few iterations, the weight factor achieves a rapid decrease to close to 1, and eventually converges quickly as the iterations proceed. In order to quantitatively evaluate the quality of the reconstructed images, verify the reliability of the methods in this section, and compare the differences between the reconstruction results of different methods, we chose to use Matlab R2018b software to perform the simulations, and selected the correlation coefficients [32] ( CC )and structural similarity [33] ( SSIM ) and contrast ratio ( CR ) as evaluation indexes, which are expressed as (2 where M is the horizontal resolution of the target image, N is the vertical resolution of the target image, ( , ) mn is the pixel coordinate,  is the covariance between them, while, 1 c and 2 c are constants. In general, the larger the CC , the better the correlation between the reconstructed image and the target image.…”

“…Non-iterative algorithms [8][9][10] do not require iterating and are suitable for real-time holographic display, making them also a current hot research direction. The random phase algorithm [11,12] is the simplest and direct non-iterative algorithm.…”