Performance Estimation of Intensity Accumulation Display by Computer-Generated Holograms

Abstract

The image generated by binary computer-generated holograms (CGHs) always suffers from serious speckle noise. Thanks to the fast frame rate of the binary spatial light modulator, the speckle can be significantly suppressed by intensity accumulation, i.e., the sequential display of multiple CGHs of the same scene. If enough randomness is added to the CGHs, the speckle noise can be mostly averaged out. Intuitively, the quality of the reconstructed image should be proportional to the number of intensity accumulation. However, there is no simple method to predict the dependence of the average noise and accumulation number, and we can only know the results after finishing the full computation. In this paper, we propose an empirical formula of the average noise based on the speckle phenomenon in a laser projector. Using this model, we have confirmed that the randomness induced by random phase is equivalent to that induced by random down-sampling for the generation of binary CGHs. In addition, if the computational efficiency is a concern, the CGH calculated with iterations is not recommended for intensity accumulation display. Finally, there is an upper-quality limit of the reconstructed image by intensity accumulation. Thus, a strategy for efficient intensity accumulation is suggested.

Review of computer-generated hologram algorithms for color dynamic holographic three-dimensional display

“…Because of the independence and randomness of the RAP, the unwanted interferences are independent and random. Inspired by this idea, researchers proposed time-average method 122 – 126 to suppress the speckle noise by summating multiple images generated by different RAP. Although this method is successful in reducing the speckle noise, it requires a SLM with high frame rate and is difficult to realize dynamic holographic display.…”

Adaptive Digital Hologram Binarization Method Based on Local Thresholding, Block Division and Error Diffusion

“…Then, the difference of these values (error values) with different weighting coefficients is distributed among the pixels that have not yet passed, adjacent to the pixel in question. There are other binarization methods, which are less frequently used and usually focus on computer-generated hologram processing: direct binary search algorithms [44][45][46], iterative techniques of kinoform generating [47,48], sampling [14,49], and others.…”