Convolutional neural networks (CNNs) have found extensive application in computer-generated holography (CGH). Nonetheless, CNNs possess limited capability to effectively model intricate geometric transformations between object points and their corresponding point spread functions due to the constrained structures of fixed convolutional kernels. In order to address this issue, we propose deformable holography (DeH) algorithm for CGH. We demonstrate that utilizing deformable convolutions enable adaptive modeling of geometric transformations. The proposed DeH algorithm generates high-quality 1080P 3D holograms in real-time, consistently outperforming existing approaches. We also validate our approach on an experimental prototype holographic display, and demonstrate DeH algorithm's ability to accurately reconstruct 3D scenes. Overall, our work introduces new possibilities of utilizing deformable convolutions for deep learning in the realm of holographic displays.
Two novel visual cryptography (VC) schemes are proposed by combining VC with single-pixel imaging (SPI) for the first time. VC can be optically implemented in both object images and illumination patterns.
In this paper, a special ciphertext-only attack (COA) scenario to the traditional double random phase encoding (DRPE) technique is proposed based on plaintext shifting. We assume the attacker can illegally manipulate the DRPE system to gain multiple ciphertexts from randomly shifted versions of the same plaintext. The plaintext image can be recovered when our proposed scenario is combined with a speckle correlation attacking method proposed in previous work. Simulation results demonstrate that our proposed scheme can successfully crack the DRPE system even when the speckle correlation method alone fails to work in the conventional single ciphertext scenario due to the small size of the plaintext image. The work in this paper reveals a severe security flaw of DRPE systems when minor position shifting of the plaintext occurs.
Joint photographic experts group (JPEG) compression standard is widely adopted for digital images. However, as JPEG encoding is not designed for holograms, applying it typically leads to severe distortions in holographic projections. In this work, we overcome this problem by taking into account the influence of JPEG compression on hologram generation in an end-to-end fashion. To this end, we introduce a novel approach to merge the process of hologram generation and JPEG compression with one differentiable model, enabling joint optimization via efficient first-order solvers. Our JPEG-aware end-to-end optimized holograms show significant improvements compared to conventional holograms compressed using JPEG standard both in simulation and on experimental display prototype. Specifically, the proposed algorithm shows improvements of 4 dB in peak signal-to-noise ratio (PSNR) and 0.27 in structural similarity (SSIM) metrics, under the same compression rate. When maintained with the same reconstruction quality, our method reduces the size of compressed holograms by about 35
In single-pixel imaging (SPI), a large number of illuminations is usually required to capture one single image. Consequently, SPI may only achieve a very low frame rate for a fast-moving object and the reconstructed images are contaminated with blur and noise. In previous works, some attempts are made to perform motion estimation between neighboring frames in a SPI video to enhance the image quality. However, the motion estimation and quality enhancement from one single image frame in dynamic SPI was seldom investigated. In this work, it assumed that some prior knowledge about the type of motion the object undergoes is known. A motion model of the target object is constructed and the motion parameters can be optimized within a search space. Our proposed scheme is different from common motion deblur techniques for photographs since the motion blur mechanism in SPI is significantly different from a conventional camera. Experimental results demonstrate that the reconstructed images with our proposed scheme in dynamic SPI have much better quality.
The rapid development of artificial intelligence (AI) facilitates various applications from all areas but also poses great challenges in its hardware implementation in terms of speed and energy because of the explosive growth of data. Optical computing provides a distinctive perspective to address this bottleneck by harnessing the unique properties of photons including broad bandwidth, low latency, and high energy efficiency. In this review, we introduce the latest developments of optical computing for different AI models, including feedforward neural networks, reservoir computing, and spiking neural networks (SNNs). Recent progress in integrated photonic devices, combined with the rise of AI, provides a great opportunity for the renaissance of optical computing in practical applications. This effort requires multidisciplinary efforts from a broad community. This review provides an overview of the state-of-the-art accomplishments in recent years, discusses the availability of current technologies, and points out various remaining challenges in different aspects to push the frontier. We anticipate that the era of large-scale integrated photonics processors will soon arrive for practical AI applications in the form of hybrid optoelectronic frameworks.
Optical computing and optical neural network have gained increasing attention in recent years because of their potential advantages of parallel processing at the speed of light and low power consumption by comparison with electronic computing. The optical implementation of the fundamental building blocks of a digital computer, i.e. logic gates, has been investigated extensively in the past few decades. Optical logic gate computing is an alternative approach to various analogue optical computing architectures. In this paper, the latest development of optical logic gate computing with different kinds of implementations is reviewed. Firstly, the basic concepts of analogue and digital computing with logic gates in the electronic and optical domains are introduced. And then a comprehensive summary of various optical logic gate schemes including spatial encoding of light field, semiconductor optical amplifiers (SOA), highly nonlinear fiber (HNLF), microscale and nanoscale waveguides, and photonic crystal structures is presented. To conclude, the formidable challenges in developing practical all-optical logic gates are analyzed and the prospects of the future are discussed.
This paper reports the concept and realization of Holographic QR (HQR) code. The digital hologram can be reconstructed optically or numerically, and deciphered with a commodity QR reader with high damage resistant capability.
In many previous works, a single-pixel imaging (SPI) system is constructed as an optical image encryption system. Unauthorized users are not able to reconstruct the plaintext image from the ciphertext intensity sequence without knowing the illumination pattern key. However, little cryptanalysis about encrypted SPI has been investigated in the past. In this work, we propose a known-plaintext attack scheme and a ciphertext-only attack scheme to an encrypted SPI system for the first time. The known-plaintext attack is implemented by interchanging the roles of illumination patterns and object images in the SPI model. The ciphertext-only attack is implemented based on the statistical features of single-pixel intensity values. The two schemes can crack encrypted SPI systems and successfully recover the key containing correct illumination patterns.
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