Perovskite single-pixel detector for dual-color metasurface imaging recognition in complex environment.

Highly efficient multi-dimensional data storage and extraction are two primary ends for the design and fabrication of emerging optical materials. Although metasurfaces show great potential in information storage due to their modulation for different degrees of freedom of light, a compact and efficient detector for relevant multi-dimensional data retrieval is still a challenge, especially in complex environments. Here, we demonstrate a multi-dimensional image storage and retrieval process by using a dual-color metasurface and a double-layer integrated perovskite single-pixel detector (DIP-SPD). Benefitting from the photoelectric response characteristics of the FAPbBr2.4I0.6 and FAPbI3 films and their stacked structure, our filter-free DIP-SPD can accurately reconstruct different colorful images stored in a metasurface within a single-round measurement, even in complex environments with scattering media or strong background noise. Our work not only provides a compact, filter-free, and noise-robust detector for colorful image extraction in a metasurface, but also paves the way for color imaging application of perovskite-like bandgap tunable materials.

Metalens and microtaper spectrometers on a fingertip.

A multi-foci metalens and a leaky-mode microtaper provide innovative platforms to achieve high-resolution, wideband light spectra in millimeter-sized devices, thereby paving new ways for the commercialization of on-fingertip spectrometers.

Feature ghost imaging for color identification.

On the basis of computational ghost imaging (CGI), we present a new imaging technique, feature ghost imaging (FGI), which can convert the color information into distinguishable edge features in retrieved grayscale images. With the edge features extracted by different order operators, FGI can obtain the shape and the color information of objects simultaneously in a single-round detection using one single-pixel detector. The feature distinction of rainbow colors is presented in numerical simulations and the verification of FGI's practical performance is conducted in experiments. Furnishing a new perspective to the imaging of colored objects, our FGI extends the function and the application fields of traditional CGI while sustaining the simplicity of the experimental setup.

Computational ghost imaging encryption with a pattern compression from 3D to 0D.

The principle of computational ghost imaging (GI) offers a potential application in optical encryption. Nevertheless, large numbers of keys composed of random or specific patterns set an obstacle to its application. Here, we propose a series of pattern compression methods based on computational GI, in which thousands of patterns are replaced by a single standard image (i.e., two-dimensional data), a sequence of numbers (i.e., one-dimensional data) or the fractional part of an irrational number (i.e., zero-dimensional data). Different pattern compression methods are tested in both simulations and experiments, and their error tolerances in encryption are further discussed. Our proposed methods can greatly reduce the pattern amount and enhance encryption security, which pushes forward the application of computational GI, especially in optical encryption.

Single-pixel imaging with Gao-Boole patterns.

Single-pixel imaging (SPI) can perceive the world using only a single-pixel detector, but long sampling times with a series of patterns are inevitable for SPI, which is the bottleneck for its practical application. Developing new patterns to reduce the sampling times might provide opportunities to address this challenge. Based on the Kronecker product of Hadamard matrix, we here design a complete set of new patterns, called Gao-Boole patterns, for SPI. Compared to orthogonal Hadamard basis patterns with elements valued as +1 or -1, our Gao-Boole patterns are non-orthogonal ones and the element values are designed as +1 or 0. Using our Gao-Boole patterns, the reconstructed quality of a target image (N × N pixels) is as high as the Hadamard one but only with half pattern numbers of the Hadamard ones, for both full sampling (N2 for Gao-Boole patterns, 2N2 for Hadamard basis patterns) and undersampling cases in experiment. Effectively reducing the patterns numbers and sampling times without sacrificing imaging quality, our designed Gao-Boole patterns provide a superior option for structural patterns in SPI and help to steer SPI toward practical imaging application.

Anti-loss-compression image encryption based on computational ghost imaging using discrete cosine transform and orthogonal patterns.

As an emerging imaging technique, computational ghost imaging (CGI) has its unique application in image encryption. However, the long imaging time and high requirement of transmitting data, both in the size of data and vulnerability of lossy compression, limit its application in the practical communications. Using discrete cosine transform to sparse bucket signals of CGI, we here propose a method by transforming the bucket signals from the sensing matrix domain to the space domain, enhancing the ability of the bucket signals (i.e., encrypted image) to resist the lossy compression. Based on the principle of CGI, we first propose to use gradient descent to find an orthogonal matrix as the encryption key, then test the performance of our method at different quality factors and undersampling rates. Both simulations and experimental results demonstrate that our encryption method shows great resistance to the traditional lossy compression methods and has good performance in the undersampling conditions. Our method provides a convenient way to transmit the bucket signals of CGI by the format that involves lossy compression and thus camouflages itself while significantly reducing the amount of data being transmitted.

Inverse computational ghost imaging for image encryption.

Computer-generated random patterns and bucket detection are two key characteristics of computational ghost imaging (GI), which offer it a potential application in optical encryption. Here, we propose an inverse computational GI scheme, in which bucket signals are firstly selected and then random patterns are calculated correspondingly. Different GI reconstruction algorithms are used to test the inverse computational GI, and the relationship between imaging quality and error ratio factor is discussed as well. Compared with computational GI, our inverse one not only has disguised bucket signals but also provides an opportunity to combine with other cryptographies, both of which enrich the GI-based encryption process and enhance the security simultaneously.

Metasurface-based key for computational imaging encryption.

Optical metasurfaces can offer high-quality multichannel displays by modulating different degrees of freedom of light, demonstrating great potential in the next generation of optical encryption and anti-counterfeiting. Different from the direct imaging modality of metasurfaces, single-pixel imaging (SPI) as a typical computational imaging technique obtains the object image from a decryption-like computational process. Here, we propose an optical encryption scheme by introducing metasurface-images (meta-images) into the encoding and decoding processes as the keys of SPI encryption. Different high-quality meta-images generated by a dual-channel Malus metasurface play the role of keys to encode multiple target images and retrieve them following the principle of SPI. Our work eliminates the conventional digital transmission process of keys in SPI encryption, enables the reusability of a single metasurface in different encryption processes, and thereby paves the way toward a high-security optical encryption between direct and indirect imaging methods.