Three-dimensional (3-D) optical image sensing and visualization technologies have been researched extensively for different applications in fields as diverse as entertainment, medical sciences, robotics, manufacturing, and defense. In many instances, the capabilities of 3-D imaging and display systems have revolutionized the progress of these disciplines, enabling new detection/display abilities that would not have been otherwise possible. As one of the promising methods in the area of 3-D sensing and display, integral imaging offers passive and relatively inexpensive way to capture 3-D information and to visualize it optically or computationally. The integral imaging technique belongs to the broader class of multiview imaging techniques and is based on a century old principle which has only been resurrected in the past decade owing to advancement of optoelectronic image sensors as well as the exponential increase in computing power. In this paper, historic and physical foundations of integral imaging are overviewed; different optical pickup and display schemes are discussed and system parameters and performance metrics are described. In addition, computational methods for reconstruction and range estimation are presented and several applications including 3-D underwater imaging, near infra red passive sensing, imaging in photon-starved environments, and 3-D optical microscopy are discussed among others.

The ability to sense, track, identify, and monitor biological micro/nanoorganisms in a real-time, automated, and integrated system is of great importance from both scientific and technological standpoints. Such a system and its possible variants would have numerous applications in a wide spectrum of fields, including defense against biological warfare, disease control, environmental health and safety, and medical treatments. In this paper, we review a comprehensive mixture of optical and computational tools developed in our group aiming at real-time sensing and recognition of biological microorganisms. Digital in-line holographic microscopy is used with both coherent and partially coherent illumination to probe the specimen interferometerically. The interference pattern is then recorded on an optoelectronic image sensor and transferred to a computer where special statistical algorithms are performed to segment, recognize, and track the microorganisms within the field of view of the microscope. The advantages of proposed holographic sensing are described compared to conventional two-dimensional imaging systems. In addition, the theoretical aspects and fundamental limitations of digital in-line holographic microscopy are discussed, which determine the relationship between system parameters and achievable performance. The proposed optical-digital integrated system for automated, real-time sensing and recognition of biological microorganisms has been deemed promising with the potential of widespread application. We demonstrate how the proposed techniques function together in a series of experiments.

Image data hiding is the hiding of invisible patterns in an image without degrading its visual quality. This hidden pattern can be visualized by overlaying the original image with watermarked ones. Also it can be visualized using a simple XNOR operation. In this paper we propose a method called dot diffusion with nonlinear thresholding (DDNT) to embed hidden data in halftone images and its modification to color halftone images which will be called CDDNT. The main advantage of this method is that the hidden pattern's intensity can be adjusted by three parameters. Also it utilizes the inherent parallelism in dot diffusion halftoning method, although it suffers from its lower visual quality than some other halftoning methods such as error diffusion.