Photopolymer materials have received a great deal of attention because they are inexpensive, self-processing materials that are extremely versatile, offering many advantages over more traditional materials. To achieve their full potential, there is significant value in understanding the photophysical and photochemical processes taking place within such materials. This paper includes a brief review of recent attempts to more fully understand what is needed to optimize the performance of photopolymer materials for Holographic Data Storage (HDS) and Self-Written Waveguides (SWWs) applications. Specifically, we aim to discuss the evolution of our understanding of what takes place inside these materials and what happens during photopolymerization process, with the objective of further improving the performance of such materials. Starting with a review of the photosensitizer absorptivity, a dye model combining the associated electromagnetics and photochemical kinetics is presented. Thereafter, the optimization of photopolymer materials for HDS and SWWs applications is reviewed. It is clear that many promising materials are being developed for the next generation optical applications media.

The application of digital holography as a viable solution to 3D capture and display technology is examined. A review of the current state of the field is presented in which some of the major challenges involved in a digital holographic solution are highlighted. These challenges include (i) the removal of the DC and conjugate image terms, which are features of the holographic recording process, (ii) the reduction of speckle noise, a characteristic of a coherent imaging process, (iii) increasing the angular range of perspective of digital holograms (iv) and replaying captured and/or processed digital holograms using spatial light modulators. Each of these challenges are examined theoretically and several solutions are put forward. Experimental results are presented that demonstrate the validity of the theoretical solutions.

It is shown experimentally that both surface tilt and in-plane translation motion can be independently estimated using the speckle photographic correlation technique by capturing consecutive images in two linear canonical transform domains (using two different quadratic phase systems). A geometric interpretation, based on use of the Wigner distribution function is presented to describe the method and a simple matrix approach, based on the ABCD matrix, is used to quantify it. It is shown that the sensitivity and dynamic range of measurement of both tilt and translation are both variable and depend on the parameters of the ABCD matrix.

It is important to be able to quantify, theoretically and experimentally, the performance of coherent digital systems, so that their suitability for a given metrology application can be assessed. Here, a free-space inline digital holographic system is investigated. To isolate the scattered object field, phase-shifting interferometry (PSI) techniques are used. Several sequential holographic measurements are made, where the phase of the reference field is stepped by a known amount relative to the scattered object field between captures. Under ideal conditions such as noise-free electronics, vibration-free environments, and perfect reference and illuminating object waves, this system will be diffraction limited. However, real systems suffer from experimental error and noise effects. In this paper, we examine a PSI digital holographic imaging system considering all prominent error sources. An experimental metric is defined that quantifies how far from the theoretical ideal a real system is performing. By carefully optimizing our system, following our recommended guidelines, we approach diffraction limited imaging, surpassing the Nyquist sampling rate of the CCD/CMOS device.

Under the scalar paraxial approximation, an optical wavefield is considered to be complex function dependent on position; i.e., at a given location in space the optical field is a complex value with an intensity and phase. The optical wavefield propagates through space and can be modeled using the Fresnel transform. Lenses, apertures, and other optical elements can be used to control and manipulate the wavefield and to perform different types of signal processing operations. Often these optical systems are described theoretically in terms of linear systems theory leading to a commonly used Fourier optics framework. This is the theoretical framework that we will assume in this manuscript. The problem which we consider is how to recover the phase of an optical wavefield over a plane in space. While today it is relatively straightforward to measure the intensity of the optical wavefield over a plane using CMOS or CCD sensors, recovering the phase information is more complicated. Here we specifically examine a variant of the problem of phase retrieval using two intensity measurements. The intensity of the optical wavefield is recorded in both the image plane and the Fourier plane. To make the analysis simpler, we make a series of important theoretical assumptions and describe how in principle the phase information can be recovered. Then, a deterministic but iterative algorithm is derived and we examine the characteristics and properties of this algorithm. Finally, we examine some of the theoretical assumptions we have made and how valid these assumptions are in practice. We then conclude with a brief discussion of the results.

The performance of many human tracking algorithms rely on accurate motion models. Due to the nature of human motion it is often difficult to determine the suitability of a chosen model. It is typically the case that over the tracking duration the characteristics of the observed motion will fit many different models. Commonly used motion models in the area of tracking include the constant position (CP), constant velocity (CV) and constant acceleration (CA) motion models. This paper applies the Kalman filtering algorithm to the problem of tracking a person's position using a finite set of different motion models. The statistical properties of the innovation sequence are used as a basis for motion model selection.