The goal of realizing image-capture techniques for ultrahigh-definition three-dimensional images has spurred research into photographic applications of incoherent digital holography (IDH), a technique in which holograms forming the basis of three-dimensional images are captured and reconstructed without using lasers or other specialized light sources. One problematic aspect of IDH is the susceptibility of low-contrast holograms to noise originating from imaging sensors and the consequent degradation of the quality of three-dimensional images. To address this challenge, we present, in this paper, a theoretical analysis of the sampling interval (the interval between digital signals captured to acquire an analog signal) needed for holographic imaging. Then, on the basis of the results of our analysis, we propose a method of adaptive spatial averaging (an algorithm for computing average values of light signals for sets of adjacent pixels in accordance with certain parameters). Our theoretical investigation demonstrates that the sampling interval required for hologram acquisition varies depending on the depth positions of imaged bodies. From this finding, we present a computational algorithm that adaptively varies spatial-averaging parameters on the basis of the depth positions of imaged bodies in acquired holograms. With the proposed algorithm, noise is successfully reduced without degrading the spatial resolution of hologram-reconstructed images.
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