Guo Li

Abstract

Hopfield neural nets are used to optimize point-oriented binary computer-generated holograms (CGHs). It can be considered as a parallel and iterative 'halftoning' process in the spatial frequency domain. The results are comparable to other iterative methods but require shorter computation times. In this process, the generation of the CGH by FFT, binarization, and IFFT is viewed as a "black box" with inputs and outputs consisting of 512 arrays containing an object of size 64 . The neural-network optimization feeds back the Fourier transform of the reconstruction error to update the neuron states, which correspond to the samples of the continuous hologram. To reduce the error of the reconstruction, the input is allowed to deviate from the original array in different specified ways. For example, a previously reported approach using Projection Onto Constraint Sets (POCS) varied only the region of the input array outside the object, while we allow the entire array to be modified, thus providing more freedom in the optimization. The method may be applied either to magnitude-only or phase-only holograms. A modification of the parallel updating function is also reported. Different optimization options are compared. Use of a practical printing model requires optimization under assumed constraints to test the convergence properties of the algorithm.

Library of Congress Subject Headings

Holography--Data processing; Fourier transformations; Image processing--Digital techniques; Neural networks (Computer science)

Publication Date

10-1-1995

Document Type

Thesis

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Advisor

Easton, Roger

Advisor/Committee Member

Eschbach, Reiner

Advisor/Committee Member

Anderson, Peter

Comments

Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works. Physical copy available through RIT's The Wallace Library at: TA1542.L522 1995

Campus

RIT – Main Campus

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