The modulation of light is important in phase holographic data storage, and different spatial light modulators have different modulation capabilities for light. In this study, a lens less non-interference phase reconstruction system based on deep learning is applied to evaluate the performance differences of different spatial light modulators in holographic data storage. The performance differences are evaluated, which include the deep learning results versus pixel crosstalk results for phased holographic optical storage.
Linear polarization holography based on tensor theory has yielded numerous intriguing discoveries and applications. Utilizing theories such as null reconstruction, applications beneficial for holographic optical storage have been realized, with dual-channel polarization multiplexing being one of them. However, previous research has found deviations in the grayscale ratios on reconstructed images compared to original images, especially when uploading grayscale images with higher levels, such as 4-level grayscale images. This study conduct experiments using different recording methods to identify the source of grayscale crosstalk from a single test image. The results indicate that the recording modes, whether co-polarized or orthogonal polarized, do not significantly disrupt amplitude recording.
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