A two-stage compression framework for computer-generated hologram (CGH) with compressed sensing (CS) and quantum-inspired neural network (QINN) is proposed. A deep learning-based CS model is applied to sample the CGH at sub-Nyquist rates to generate measurements, and its more compact and representative information using interblock information is further extracted by QINN. With two-stage compression framework, the CGH is greatly compressed and suitable for storage and transmission. Experimental results demonstrate that our proposed two-stage compression framework can preserve more important holographic information during the compression, thus significantly improving the overall quality of reconstructed images compared to only using CS or QINN.
An optimization scheme based on wide-activated deep residual network super-resolution (WDSR) is proposed for the stereoscopic image synthesis algorithm of a cylindrical grating display. Unlike the traditional synthesis scheme, the WDSR algorithm is used to replace, partially or fully, the interpolation algorithm to reduce the antialiasing effect of the image. Thereafter, the viewpoint mapping process is accelerated based on the subpixel mapping table by making masks. Subsequently, a stereoscopic image with a better quality is obtained. The experimental results demonstrate that the proposed scheme can improve the signal-to-noise ratio and quality of the stereoscopic composite image when the same subviewpoint image is used for verification.
A method for compressing computer-generated holograms (CGHs) using genetic algorithm optimized quantum-inspired neural network is proposed. Genetic algorithm is a global optimization algorithm, which can provide better initial weights for the quantum-inspired neural network. The global optimization ability of genetic algorithm is combined with the local optimization ability of the quantum-inspired neural network enables the network to achieve better convergence effects. Under different compression ratios, CGHs are compressed by the genetic algorithm optimized quantum-inspired neural network and the quantum-inspired neural network respectively, and Fresnel transform technology is used to reconstruct the decompressed CGHs. The experimental results show that the genetic algorithm optimized quantuminspired neural network can obtain better quality reconstructed images than the quantum-inspired neural network while using fewer learning iterations.
We propose an attention-based deep convolutional neural network for computer generated hologram (CGH) compression, where a channel attention mechanism is applied to both computer generated hologram compression and reconstruction. By applying deep convolutional neural networks in the compression process, we can extract more compact and representative information than bicubic interpolation. Additionally, a channel attention mechanism is applied to selectively emphasize informative features and suppress less useful ones for both CGH compression and reconstruction. By employing attention mechanisms to enhance the feature representation ability of deep convolutional neural networks, we can further improve the performance of the reconstructed computer generated hologram. Experimental results show our method can better recover the compressed computer generated hologram than only employing convolutional neural networks in the reconstruction process.
This paper through rigorous mathematical reasoning proves that four-step phase-shifting interference algorithm in phase error caused by nonlinear response inhibition system is better than that of three-step phase-shifting interference algorithm. It makes up the theoretical deficiency of previous relevant studies. And the reconstruction image quality of four-step phaseshifting algorithm is better than that of three-step phase-shifting interference. System simulation and digital holographic experiments have been carried out to prove the correctness and rigor of the above theoretical reasoning. In addition, through the analysis of experimental results, it is found that the experimental error is caused by a large number of high-frequency noise signals.
We propose an optimized initial weight scheme in a quantum-inspired neural network for compressing computer-generated holograms (CGHs). An optimized initial weight generation strategy is applied to accelerate the compression process. The pixel blocks’ complexity distribution of CGH is analyzed, and the parallel quantum neural network structure is used to compress the image pixel blocks. A deep convolutional neural network with residual learning is also adopted for improving the quality of the reconstructed image. The experimental results have shown that the compression iterations are reduced by using the optimized initial weight, and the reconstructed image quality of the compressed CGH is improved using the parallel quantum-inspired neural network structure and the deep convolutional neural network with residual learning.
A method for computer-generated hologram (CGH) compression and transmission using a quantum back-propagation neural network (QBPNN) is proposed, with the Fresnel transform technique adopted for image reconstruction of the compressed and transmitted CGH. Experiments of simulation were conducted to compare the reconstructed images from CGHs processed using a QBPNN with those processed using a back-propagation neural network (BPNN) at the optimal learning coefficients. The experimental results show that the method using a QBPNN could produce reconstructed images with a better quality than those obtained using a BPNN despite the use of fewer learning iterations at the same compression ratio.
An optimization scheme based on a genetic algorithm (GA) is proposed for kinoform synthesis. Unlike conventional optimization schemes, the initial kinoforms here are obtained by Fourier transform of the original image with random phase masks. The phase masks are then optimized by GA in order to reduce the reconstruction noise caused by amplitude negligence and phase quantization. Compared with the conventional methods of the genetic algorithm, in which optimization is directly performed to the kinoforms, the scheme can significantly improve the convergence and reduce the computation cost.
We propose a new scheme of computer-generated hologram (CGH) watermarking to resist rotation and scaling. To embed the inverse log-polar mapping of a mark pattern's CGH into a cover image, the twin image of the mark pattern can be directly reconstructed by fast Fourier transformation from the log-polar mapping of the watermarked image after rotation and scaling, not requiring a registration step in the extracting procedure. In an experiment, the information position of the twin image is located in the high-frequency domain and the redundant information of the low-frequency component is properly eliminated, so the contrast of the twin image is appropriately enhanced and the basic information of the mark pattern is effectively preserved to be recognized. The experimental results show that the mark-pattern's information can be effectively reconstructed when the watermarked image is scaled by 0.5 to 2 or rotated by any angle, so this watermarking scheme is effectively verified by experiment.
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