Leveraging an optical system for image encryption is a promising approach to information security since one can enjoy parallel, high-speed transmission, and low-power consumption encryption features. However, most existing optical encryption systems involve a critical issue that the dimension of the ciphertexts is the same as the plaintexts, which may result in a cracking process with identical plaintext-ciphertext forms. Inspired by recent advances in computational neuromorphic imaging (CNI) and speckle correlography, a neuromorphic encryption technique is proposed and demonstrated through proof-of-principle experiments. The original images can be optically encrypted into event-stream ciphertext with a high-level information conversion form. To the best of our knowledge, the proposed method is the first implementation for event-driven optical image encryption. Due to the high level of encryption data with the CNI paradigm and the simple optical setup with a complex inverse scattering process, our solution has great potential for practical security applications. This method gives impetus to the image encryption of the visual information and paves the way for the CNI-informed applications of speckle correlography.
The assessment of microplastics (MPs) pollution and water quality monitoring raise a lot of attention in recent years. Discriminative methods are highly needed for quick and accurate in situ MP detections. Digital holography records the wavefront information of the objects and contains the morphology, refractive index, and roughness information. Polarization imaging inspects the optical anisotropy of MPs, which is related to their birefringence and material characteristics. In this work, we explore the capability of holographic and polarization imaging for the identification of MPs. The computed features, such as the angle of polarization (AoP) and degree of linear polarization (DoLP), show distinguishable characteristics of MPs. We inspect the method feasibility on MP classification as well as biological and natural particles. The proposed method shows potential use in real-time, non-contact in situ MPs detection and water pollution monitoring.
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