Abstract: The present disclosure provides improved systems and methods for automated cell identification/classification. More particularly, the present disclosure provides advantageous systems and methods for automated cell identification/classification using shearing interferometry with a digital holographic microscope. The present disclosure provides for a compact, low-cost, and field-portable 3D printed system for automatic cell identification/classification using a common path shearing interferometry with digital holographic microscopy. This system has demonstrated good results for sickle cell disease identification with human blood cells. The present disclosure provides that a robust, low cost cell identification/classification system based on shearing interferometry can be used for accurate cell identification.

Abstract: Described herein is an object recognition system in low illumination conditions. A 3D InIm system can be trained in the low illumination levels to classify 3D objects obtained under low illumination conditions. Regions of interest obtained from 3D reconstructed images are obtained by de-noising the 3D reconstructed image using total-variation regularization using an augmented Lagrange approach followed by face detection. The regions of interest are then inputted into a trained CNN. The CNN can be trained using 3D InIm reconstructed under low illumination after TV-denoising. The elemental images were obtained under various low illumination conditions having different SNRs. The CNN can effectively recognize the 3D reconstructed faces after TV-denoising.

Abstract: Portable common path shearing interferometry-based holographic microscopy systems. The system includes a light source, a sample holder, a microscope objective lens, a shear plate and an imaging device positioned in a common path shearing interferometry configuration. A housing is configured to receive and hold the shear plate and maintain a position of the shear plate relative to the microscope objective lens.

Abstract: Systems and methods for optical sensing, visualization and detection in media (e.g., turbid media; turbid water; fog; non-turbid media). A light source and an image sensor are positioned in turbid media or external to the turbid media with the light source within a field of view of the image sensor array. Temporal optical signals are transmitted through the turbid media via the light source and multiple perspective video sequence frames are acquired via the image sensor array of light propagating through the turbid media. A three-dimensional image is reconstructed from each frame and the reconstructed three-dimensional images are combined to form a three-dimensional video sequence. The transmitted optical signals are detected from the three-dimensional video sequence by applying a multi-dimensional signal detection scheme.

Abstract: Described herein is an object recognition system in low illumination conditions. A 3D InIm system can be trained in the low illumination levels to classify 3D objects obtained under low illumination conditions. Regions of interest obtained from 3D reconstructed images are obtained by de-noising the 3D reconstructed image using total-variation regularization using an augmented Lagrange approach followed by face detection. The regions of interest are then inputted into a trained CNN. The CNN can be trained using 3D InIm reconstructed under low illumination after TV-denoising. The elemental images were obtained under various low illumination conditions having different SNRs. The CNN can effectively recognize the 3D reconstructed faces after TV-denoising.

Abstract: Systems and methods for optical sensing, visualization and detection in media (e.g., turbid media; turbid water; fog; non-turbid media). A light source and an image sensor are positioned in turbid media or external to the turbid media with the light source within a field of view of the image sensor array. Temporal optical signals are transmitted through the turbid media via the light source and multiple perspective video sequence frames are acquired via the image sensor array of light propagating through the turbid media. A three-dimensional image is reconstructed from each frame and the reconstructed three-dimensional images are combined to form a three-dimensional video sequence. The transmitted optical signals are detected from the three-dimensional video sequence by applying a multi-dimensional signal detection scheme.

Abstract: Embodiments of the present disclosure include systems and methods for cell identification using a lens-less cell identification sensor. Randomly distributed cells can be illuminated by a light source such as a laser. The object beam can be passed through one or more diffusers. Pattern recognition is applied on the captured optical signature to classify the cells. For example, features can be extracted and a trained classifier can be used to classify the cells. The cell classes can be accurately identified even when multiple cells of the same class are inspected.

Abstract: Portable common path shearing interferometry-based holographic microscopy systems are disclosed herein. In one embodiment, a system is configured for positioning a laser light source, a sample holder, a microscope objective lens, a shear plate and the imaging device in a common path shearing interferometry configuration. In some embodiments, the systems are relatively small and lightweight and exhibit good temporal stability. In one embodiment, a system for automatic cell identification and visualization using digital holographic microscopy using an augmented reality display device can include an imaging device mounted to an augmented reality display device, configured to capture one or more images of the hologram of the sample disposed on the sample holder illuminated by a beam. The augmented reality display device can include a display and can be configured to render a pseudo-colored 3D visualization of the sample and information associated with the sample, on the display.

Abstract: The present disclosure provides improved systems and methods for automated cell identification/classification. More particularly, the present disclosure provides advantageous systems and methods for automated cell identification/classification using shearing interferometry with a digital holographic microscope. The present disclosure provides for a compact, low-cost, and field-portable 3D printed system for automatic cell identification/classification using a common path shearing interferometry with digital holographic microscopy. This system has demonstrated good results for sickle cell disease identification with human blood cells. The present disclosure provides that a robust, low cost cell identification/classification system based on shearing interferometry can be used for accurate cell identification.

Abstract: Embodiments of the present disclosure include systems and methods for cell identification using a lens-less cell identification sensor. Randomly distributed cells can be illuminated by a light source such as a laser. The object beam can be passed through one or more diffusers. Pattern recognition is applied on the captured optical signature to classify the cells. For example, features can be extracted and a trained classifier can be used to classify the cells. The cell classes can be accurately identified even when multiple cells of the same class are inspected.

Abstract: An optical security method for object authentication using photon-counting encryption implemented with phase encoded QR codes. By combining the full phase double-random-phase encryption with photon-counting imaging method and applying an iterative Huffman coding technique, encryption and compression of an image containing primary information about the object is achieved. This data can then be stored inside of an optically phase-encoded QR code for robust read out, decryption, and authentication. The optically encoded QR code is verified by examining the speckle signature of the optical masks using statistical analysis.

Abstract: An optical security method for object authentication using photon-counting encryption implemented with phase encoded QR codes. By combining the full phase double-random-phase encryption with photon-counting imaging method and applying an iterative Huffman coding technique, encryption and compression of an image containing primary information about the object is achieved. This data can then be stored inside of an optically phase-encoded QR code for robust read out, decryption, and authentication. The optically encoded QR code is verified by examining the speckle signature of the optical masks using statistical analysis.