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Abstract #4754

De-Aliasing for Under-sampling in Phase Scrambling Fourier Transform Imaging using Alias-free Reconstruction and Deep Convolutional Neural Network

Satoshi ITO1 and Tsukasa SAITO1

1Utsunomiya University, Utsunomiya, Japan

Alias-free image reconstruction is feasible in phase scrambling Fourier transform imaging. When small down-scaling factor is used in that method, the size of reconstructed images become small and aliased image are separated in the scaled space. In this work, a new fast imaging method in which aliasing artifacts due to under-sampling of signal is removed 2-steps; one is down-scaled space introduced by alias-free reconstruction and the second is the denoising using deep convolution network. It was shown that proposed method provide higher PSNR images compared to random sampling compressed sensing and has an advantage in low sampling rate image acquisition.

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