Recovery of Piecewise Smooth Images from Few Fourier Samples
This work addresses image reconstruction challenges in domains like medical imaging (e.g., MRI) by enabling recovery from sparse data, though it appears incremental as it builds on existing annihilation-based methods.
The authors tackled the problem of reconstructing piecewise smooth images from limited Fourier samples by introducing a Prony-like method that exploits annihilation relations from partial derivatives, and they demonstrated its application in recovering MRI phantoms from few low-resolution Fourier samples.
We introduce a Prony-like method to recover a continuous domain 2-D piecewise smooth image from few of its Fourier samples. Assuming the discontinuity set of the image is localized to the zero level-set of a trigonometric polynomial, we show the Fourier transform coefficients of partial derivatives of the signal satisfy an annihilation relation. We present necessary and sufficient conditions for unique recovery of piecewise constant images using the above annihilation relation. We pose the recovery of the Fourier coefficients of the signal from the measurements as a convex matrix completion algorithm, which relies on the lifting of the Fourier data to a structured low-rank matrix; this approach jointly estimates the signal and the annihilating filter. Finally, we demonstrate our algorithm on the recovery of MRI phantoms from few low-resolution Fourier samples.