Holographic Image Generation Using Iterative Phase Quantization
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Solution Overview
Problem
Traditional methods for generating holographic images, such as the Gerchberg-Saxton Algorithm and Iterative Fourier Transform Algorithm, face issues with computation intensity, stagnation, and poor convergence, leading to low-quality images with noise, making real-time display challenging.
Innovation Solution
A method involving holographic transformation, phase quantization, and inverse transformation, with iterative amplitude phase constraining to achieve a target holographic image, utilizing a signal processor with holographic transformation, phase quantization, inverse holographic transformation, and determination units to ensure high-quality, low-noise image generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional iterative algorithms (GS, IFTA) are used to generate holographic images, then the holographic image can be reconstructed, but the computation is too intensive and the operation time is too long
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing the phase distribution data of the target image before the actual holographic generation process. The complex amplitude constraint algorithm pre-processes the target image to obtain phase distribution information, which is then used during iterative reconstruction to significantly reduce computation time. This preliminary preparation eliminates the need for repeated full-image computations during iteration.
Solution Approach 2:
The patent segments the holographic image generation process into distinct functional modules: phase quantization unit, complex amplitude constraint unit, and iterative reconstruction unit. The segmentation is achieved by dividing the complex amplitude into separate phase and amplitude components that can be independently processed and constrained, allowing parallel computation and reducing overall processing time.
2Manufacturing precision
If the number of iterations is increased to improve image quality, then more computation is required and the algorithm easily stagnates
Solution Approach 1:
The patent implements feedback by continuously monitoring the convergence of the iterative reconstruction process. The complex amplitude constraint algorithm compares the reconstructed image with the target image at each iteration step and adjusts the phase distribution accordingly. When the error between reconstructed and target images falls below a threshold or stagnation is detected, the iteration automatically terminates, preventing unnecessary computations.
Solution Approach 2:
The patent changes parameters by dynamically adjusting the constraint strength and iteration step size during the reconstruction process. The complex amplitude constraint modifies the phase distribution parameters based on the current reconstruction error, allowing the algorithm to converge faster with fewer iterations while maintaining image quality.
3Productivity
If traditional algorithms are used, then holographic transformation can be performed, but noise increases and image quality decreases
Solution Approach 1:
The patent applies preliminary anti-action by pre-constraining the complex amplitude of the target image to prevent noise accumulation during iteration. The complex amplitude constraint unit pre-processes the target image to establish accurate phase distribution, which acts as a guide to prevent the reconstructed image from deviating into noisy solutions. This preliminary constraint counteracts the noise-generating tendency of traditional algorithms before iteration begins.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables rapid and efficient processing and display of real-time holographic images with high contrast and low noise, allowing for free adjustment of imaging distance.
Implementation Method 1
The spatial light modulator SLM is configured to utilize the target holographic image obtained by the signal processor as a source of image, and apply the target holographic image into incident beam of the light source device. The holographic reconstructed image is obtain by performing transmission diffraction transformation
Implementation Method 2
The holographic reconstructed image is obtain by performing transmission diffraction transformation and selecting operation with the Fourier lens and the spatial filter
Data Source
AI summary
A method for generating a holographic image, a signal processor, a holographic image display device, a wearable apparatus, and an onboard head-up display apparatus. The method comprises: performing holographic transformation on the basis of a target amplitude phase distribution of a target image to obtain a holographic phase image; performing phase quantization of the holographic phase image to obtain a quantized holographic image; performing inverse holographic transformation of the quantized holographic image to obtain a reconstructed image; if the reconstructed image satisfies a preset condition, determining that the quantized holographic image is a target holographic image; if not, constraining the amplitude phase of the reconstructed image and, on the basis of the amplitude phase constrained image, continuing iteration. The present method can rapidly and effectively implement monochrome or multi-colour high contrast ratio, low noise real-time holographic image generation and display, and the imaging distance can be freely adjusted.