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.
KEYWORDS: High dynamic range imaging, High dynamic range image sensors, Sensors, Imaging systems, Temporal resolution, Cameras, Biomedical optics, Biological imaging, Super resolution, Light sources and illumination
The widespread presence and use of visual data highlight the fact that conventional frame-based electronic sensors may not be well-suited for specific situations. For instance, in many biomedical applications, there is a need to image dynamic specimens at high speeds, even though these objects occupy only a small fraction of the pixels within the entire field of view. Consequently, despite capturing them at a high frame rate, many resulting pixel values are uninformative and therefore discarded during subsequent computations. Neuromorphic imaging, which makes use of an event sensor that responds to changes in pixel intensities, is ideally suitable for detecting such fast-moving objects. In this work, we outline the principle of such detectors, demonstrate their use in a computational imaging setting, and discuss the computational algorithms to process such event data for a variety of applications.
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