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In computed tomography (CT), there are many situations where reconstruction has to be performed with sparse-view data. In sparse-view CT imaging, strong streak artifacts may appear in conventionally reconstructed images due to limited sampling rate that compromises image quality. Compressed sensing (CS) algorithm has shown potential to accurately recover(More)
Undersampling k-space data is an efficient way to speed up the magnetic resonance imaging (MRI) process. As a newly developed mathematical framework of signal sampling and recovery, compressed sensing (CS) allows signal acquisition using fewer samples than what is specified by Nyquist-Shannon sampling theorem whenever the signal is sparse. As a result, CS(More)
This paper presents a novel error-free (infinite-precision) architecture for the fast implementation of both 2-D Discrete Cosine Transform and Inverse DCT. The architecture uses a new algebraic integer quantization of a 1-D radix-8 DCT that allows the separable computation of a 2-D DCT without any intermediate number representation conversions. This is a(More)
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