We present a new algorithm designed to improve the signal to noise ratio (SNR) of point and extended source detections in direct imaging data. The novel part of our method is that it finds the linear combination of the science images that best match counterpart images with signal removed from suspected source regions. The algorithm, based on the Locally Optimized Combination of Images (LOCI) method, is called Matched LOCI or MLOCI. We show using data obtained with the Gemini Planet Imager (GPI) and Near-Infrared Coronagraphic Imager (NICI) that the new algorithm can improve the SNR of point source detections by 30-400% over past methods. We also find no increase in false detections rates. No prior knowledge of candidate companion locations is required to use MLOCI. While non-blind applications may yield linear combinations of science images which seem to increase the SNR of true sources by a factor > 2, they can also yield false detections at high rates. This is a potential pitfall when trying to confirm marginal detections or to re-detect point sources found in previous epochs. Our findings are relevant to any method where the coefficients of the linear combination are considered tunable, e.g. LOCI and Principal Component Analysis (PCA). Thus we recommend that false detection rates be analyzed when using these techniques.
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