Photon-counting imaging is integrated with optical encryption for information authentication. An image is doublerandom-phase encrypted, and a photon-limited encrypted image is obtained. The photon-counting encrypted image is generated with few photons and appears sparse; however, we show that it has sufficient information for decryption and authentication. The decrypted image cannot be easily visualized so that an additional layer of information protection is achieved. The authentication is carried out by recognition algorithms. This approach may make the verification process more robust against attacks. To the best of our knowledge, this is the first report on integrating photon-counting imaging and encryption for authentication.

In this Letter, we present a three-dimensional (3D) photon counting double-random-phase encryption (DRPE) technique using passive integral imaging. A 3D photon counting DRPE can encrypt a 3D scene and provides more security and authentications due to photon counting Poisson nonlinear transformation on the encrypted image. In addition, 3D imaging allows verification of the 3D object at different depths. Preliminary results and performance evaluation have been presented.

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In this Letter, we present results for detecting and recognizing 3D objects in photon counting images using integral imaging with maximum average correlation height filters. We show that even under photon starved conditions objects may be automatically recognized in passively sensed 3D images using advanced correlation filters. We show that the proposed filter synthesized with ideal training images can detect and recognize a 3D object in photon counting images, even in the presence of occlusions and obscuration.

We propose a new estimation method for 3D object reconstruction using photon-counting integral imaging. Earlier studies used maximum likelihood estimation (MLE) as a classical statistical method to reconstruct 3D images from photon-counting elemental images. We use an alternative statistical method known as the Bayesian method, which is more flexible and may perform better than MLE in terms of the mean square error (MSE) metric. The performance of the new reconstruction method is illustrated and compared with MLE by using the MSE. To the best of our knowledge, this is the first report to use the Bayesian method for 3D reconstruction of photon-counting integral imaging.