The interaction between light and matter, as light traverses through a material, will invariably leave an information imprint on the propagating light. This phenomenon is utilized in sensing applications where the perturbed light is used to deduce undetermined characteristics of a sample. Another interesting application of this imprinting is in the synthesis of light with designed characteristics using engineered modifications of the material's optical properties. Arising from fundamentally similar phenomena, it is thus not surprising that, over time, techniques originally designed for sensing applications eventually find their way into engineered light synthesis. Holography is a classic example of this cross-over. The term “holography” was coined to reflect the technique's capacity to record the full information from a sensing wavefront's phase and amplitude to preserve information about the perturbing material being studied. Later, computer holography was invented to synthesize light using holograms that are mathematically or iteratively determined, instead of being optically recorded. Far from being a trivial exercise, the synthesis application of a sensing technique is usually faced with a different set of theoretical and experimental hurdles that can potentially offer new degrees of design freedom to enhance performance. Computer holography, for instance, can minimize the typical twin image and spurious zero-order problems of conventional holography to achieve superior diffraction fidelity.
In this chapter, we will explore how the generalized phase contrast method can be used for wavefront synthesis. In the previous chapters we used the GPC framework to optimize output conditions under the constraint of unknown input wavefront phase disturbances. We saw that when a CPI is applied to wavefront sensing or the visualization of unknown phase objects, the GPC method specifies the filter phase and aperture size parameters for achieving optimal performance in extracting and displaying the phase information carried by an incoming wavefront. The capacity to optimize output conditions even when constrained by unknown inputs in sensing applications indicates that we can expect a much-enhanced performance in GPC-based synthesis since we can exploit the additional freedom to modify the input parameters. We will now explore how GPC can be used to find optimal input and filter parameters to synthesize wavefronts possessing desired output characteristics.
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(2009). GPC-Based Wavefront Engineering. In: Generalized Phase Contrast. Springer Series in Optical Sciences, vol 146. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2839-6_6
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