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Abstract #4676

Super-resolution based on the Signal Extrapolation in Phase scrambling Fourier Transform Imaging using Deep Convolutional Neural Network

Satoshi ITO1

1Utsunomiya University, Utsunomiya, Japan

The signal obtained in phase scrambling Fourier transform imaging can be extrapolated beyond sampling length after data acquisition like Half-phase encoding method. To realize the method for phase varied images, precise phase distribution map is required. In this paper, a new post-processing super resolution in PSFT imaging is proposed in which deep convolution neural network (CNN) is used and phase map is not required. Simulation and experimental results showed that spatial resolution was fairly improved with signal extrapolation and the improvement of spatial resolution is proportional to the strength of phase scrambling coefficient.

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