分类： 光学 >> 量子光学 提交时间： 2023-02-19
Qi Zhao
Huanhao Li
Zhipeng Yu
Chi Man Woo
Tianting Zhong
Shengfu Cheng
Yuanjin Zheng
Honglin Liu
Jie Tian
Puxiang Lai
摘要： Face recognition has recently become ubiquitous in many scenes for authentication or security purposes. Meanwhile, there are increasing concerns about the privacy of face images, which are sensitive biometric data that should be carefully protected. Software-based cryptosystems are widely adopted nowadays to encrypt face images, but the security level is limited by insufficient digital secret key length or computing power. Hardware-based optical cryptosystems can generate enormously longer secret keys and enable encryption at light speed, but most reported optical methods, such as double random phase encryption, are less compatible with other systems due to system complexity. In this study, a plain yet high-efficient speckle-based optical cryptosystem is proposed and implemented. A scattering ground glass is exploited to generate physical secret keys of gigabit length and encrypt face images via seemingly random optical speckles at light speed. Face images can then be decrypted from the random speckles by a well-trained decryption neural network, such that face recognition can be realized with up to 98% accuracy. The proposed cryptosystem has wide applicability, and it may open a new avenue for high-security complex information encryption and decryption by utilizing optical speckles.
分类： 光学 >> 量子光学 提交时间： 2023-02-19
摘要： Optical logic gates are fundamental blocks of optical computing to accelerate information processing. While significant progress has been achieved in recent years, existing implementations typically rely on dedicated structures that are predesigned to modulate the phases and intensities of optical beams accurately for specific logic functions. Thus, these optical gates usually lack reconfigurability and are incapable within or through dynamic complex media/environment, such as fog and turbid water. In this work, as a conceptual demonstration, we propose reconfigurable optical logic operations through scattering media with transmission matrix-based wavefront shaping. A light beam is reflected by a spatial light modulator divided into several subregions functioning as logic units, with each displayed with predetermined wavefronts via transmission matrix-based wavefront shaping. Each modulated wavefront transmits through the scattering medium to form a desired light field. The interference of these light fields generates bright optical focus at pre-assigned locations, representing different logic states. As a proof of concept, we experimentally demonstrate five basic logic functions (AND, OR, NOT, NAND, NOR). As the transmission matrix of the scattering medium/system can be measured instantly to adapt to environment perturbation, the method, if further engineered, opens new venues towards reconfigurable optical logic computing in a dynamically complex environment.
分类： 光学 >> 量子光学 提交时间： 2023-02-19
Xuyu Zhan
Jingjing Gao
Yu Gan
Chunyuan Song
Dawei Zhang
Songlin Zhuang
Shensheng Han
Puxiang Lai
Honglin Liu
摘要： Scattering of light in complex media scrambles optical wavefronts and breaks the principles of conventional imaging methods. For decades, researchers have endeavored to conquer the problem by inventing approaches such as adaptive optics, iterative wavefront shaping, and transmission matrix measurement. That said, imaging through/into thick scattering media remains challenging to date. With the rapid development of computing power, deep learning has been introduced and shown potentials to reconstruct target information through complex media or from rough surfaces. But it also fails once coming to optically thick media where ballistic photons become negligible. Here, instead of treating deep learning only as an image extraction method, whose best-selling advantage is to avoid complicate physical models, we exploit it as a tool to explore the underlying physical principles. By adjusting the weights of ballistic and scattered photons through a random phasemask, it is found that although deep learning can extract images from both scattered and ballistic light, the mechanisms are different: scattering may function as an encryption key and decryption from scattered light is key sensitive, while extraction from ballistic light is stable. Based on this finding, it is hypothesized and experimentally confirmed that the foundation of the generalization capability of trained neural networks for different diffusers can trace back to the contribution of ballistic photons, even though their weights of photon counting in detection are not that significant. Moreover, the study may pave an avenue for using deep learning as a probe in exploring the unknown physical principles in various fields.
分类： 光学 >> 量子光学 提交时间： 2023-02-19
Xuyu Zhang
Jingjing Gao
Yu Gan
Chunyuan Song
Dawei Zhang
Songlin Zhuang
Shensheng Han
Puxiang Lai
Honglin Liu
摘要： A channel should be built to transmit information from one place to another. Imaging is 2 or higher dimensional information communication. Conventionally, an imaging channel comprises a lens and free spaces of its both sides. The transfer function of each part is known; thus, the response of a conventional imaging channel is known as well. Replacing the lens with a scattering layer, the image can still be extracted from the detection plane. That is to say, the scattering medium reconstructs the channel for imaging. Aided by deep learning, we find that different from the lens there are different channels in a scattering medium, i.e., the same scattering medium can construct different channels to match different manners of source encoding. Moreover, we found that without a valid channel the convolution law for a shift-invariant system, i.e., the output is the convolution of its point spread function (PSF) and the input object, is broken, and information cannot be transmitted onto the detection plane. In other words, valid channels are essential to transmit image information through even a shift-invariant system.
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