Reconstruction Of A Univariate Discrete Function From The Magnitude Of Its Fourier Transform
In many branches of Physics and Engineering one comes across the problem of reconstructing a function $f$ using the Fourier transform $F$, when only partial information about the transform and the function is available. One of the most common examples is to reconstruct $f$ when only the magnitude $|...
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| Format: | Others |
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OpenSIUC
2009
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| Online Access: | https://opensiuc.lib.siu.edu/dissertations/12 https://opensiuc.lib.siu.edu/cgi/viewcontent.cgi?article=1012&context=dissertations |
| Summary: | In many branches of Physics and Engineering one comes across the problem of reconstructing a function $f$ using the Fourier transform $F$, when only partial information about the transform and the function is available. One of the most common examples is to reconstruct $f$ when only the magnitude $|f|$ of the function and the magnitude $|F|$ of the Fourier transform are known. This problem occurs in electron microscopy and wavefront sensing. Another problem which occurs in astronomy and crystallography is to reconstruct $f$ when only $|F|$ and some constraints on $f$, e.g., $f \geq 0$, are available. In this paper we study the latter problem in a context where $f$ is univariate and discrete. We make use of Fienup's analysis and adapt the Gerchberg-Saxton algorithm to our problem. We devise ways to eliminate indeterminacy and we suggest ways to improve the rate of convergence of this algorithm. |
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