CLOUD REMOVAL FROM SENTINEL-2 IMAGE TIME SERIES THROUGH SPARSE RECONSTRUCTION FROM RANDOM SAMPLES

MR Image Reconstruction from Sparse Radial Samples Using Bregman Iteration

T-C. Chang, L. He, T. Fang Siemens Corporate Research, Inc., Princeton, NJ, United States, Department of Mathematics, UCLA, Los Angeles, CA, United States Introduction Many applications in magnetic resonance imaging (MRI) require very short scan time while the image reconstruction can be performed off-line. To this end, during the scanning process it is necessary to sample the frequency plane (...

Image Reconstruction from Incomplete Data

Image Reconstruction from Incomplete Data

Basis Identification from Random Sparse Samples

This article treats the problem of learning a dictionary providing sparse representations for a given signal class, via l1 minimisation. The problem is to identify a dictionary Φ from a set of training samples Y knowing that Y = ΦX for some coefficient matrix X. Using a characterisation of coefficient matrices X that allow to recover any basis as a local minimum of an l1 minimisation problem, i...

Image reconstruction from photon sparse data

We report an algorithm for reconstructing images when the average number of photons recorded per pixel is of order unity, i.e. photon-sparse data. The image optimisation algorithm minimises a cost function incorporating both a Poissonian log-likelihood term based on the deviation of the reconstructed image from the measured data and a regularization-term based upon the sum of the moduli of the ...