We propose to use low-rank matrix approximation using the component-wise
L1-norm for direct imaging of exoplanets. Exoplanet detection by direct imaging
is a challenging task for three main reasons: (1) the host star is several
orders of magnitude brighter than exoplanets, (2) the angular distance between
exoplanets and star is usually very small, and (3) the images are affected by
the noises called speckles that are very similar to the exoplanet signal both
in shape and intensity. We first empirically examine the statistical noise
assumptions of the L1 and L2 models, and then we evaluate the performance of
the proposed L1 low-rank approximation (L1-LRA) algorithm based on visual
comparisons and receiver operating characteristic (ROC) curves. We compare the
results of the L1-LRA with the widely used truncated singular value
decomposition (SVD) based on the L2 norm in two different annuli, one close to
the star and one far away.Comment: 13 pages, 4 figures, BNAIC/BeNeLearn 202