Deep Compressed Imaging via Optimized-Pattern Scanning

Learned lensless 3D camera

Single-shot three-dimensional (3D) imaging with compact device footprint, high imaging quality, and fast processing speed is challenging in computational imaging. Mask-based lensless imagers, which replace the bulky optics with customized thin optical masks, are portable and lightweight, and can recover 3D object from a snap-shot image. Existing lensless imaging typically requires extensive calibration of its point spread function and heavy computational resources to reconstruct the object. Here we overcome these challenges and demonstrate a compact and learnable lensless 3D camera for real-time photorealistic imaging. We custom designed and fabricated the optical phase mask with an optimized spatial frequency support and axial resolving ability. We developed a simple and robust physics-aware deep learning model with adversarial learning module for real-time depth-resolved photorealistic reconstructions. Our lensless imager does not require calibrating the point spread function and has the capability to resolve depth and "see-through" opaque obstacles to image features being blocked, enabling broad applications in computational imaging.

Block compressive sensing chaotic embedded encryption for MCF-OFDM transmission system

In this paper, we propose a block compressive sensing (BCS) based chaotic embedded encryption scheme for multi-core fiber orthogonal frequency division multiplexing (MCF-OFDM) system. BCS technology is used to recover the entire desired information from the small amounts of data. Meanwhile, a four-dimensional discrete chaotic encryption model generates four masking factors, which are respectively used for coefficient random permutation (CRP), measurement matrix, diffusion and singular value decomposition (SVD) embedding to achieve ultra-high security encryption of four different dimensions. In terms of compressive sensing, CRP can make the discrete cosine transform (DCT) coefficient distribute randomly to improve the sampling efficiency of BCS. Compared with the data without compressive sensing, the data volume is reduced by 75%. In chaotic encryption, SVD technology embeds secret images of noise-like after initial encryption into carrier images to generate encrypted images with visual security. The key space reaches 10¹²⁰ and it realizes the dual protection of source image data and external representation. The proposed scheme using a 2km 7-core optical fiber achieves a 78.75 Gb/s transmission of encrypted OFDM signals. The received optical power is greater than -14 dBm, and the bit error rate (BER) of core1-core7 is lower than 10^-3. When the compression ratio sets to 0.25 and the attack range of encrypted data is up to 30%, the image can still recover the outline and general information. The experimental results show that this scheme can improve the security performance and reduce the complexity of information transmission system. Furthermore, the scheme combines The BCS chaotic embedded encryption technology with MCF-OFDM system, which has a good application prospect in the future optical networks.