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We describe Algorithms for Calibration, Optimized Registration, and Nullingthe Star in Angular Differential Imaging (ACORNS-ADI), a new, parallelizedsoftware package to reduce high-contrast imaging data, and its application todata from the SEEDS survey. We implement several new algorithms, including amethod to register saturated images, a trimmed mean for combining an imagesequence that reduces noise by up to ~20%, and a robust and computationallyfast method to compute the sensitivity of a high-contrast observationeverywhere on the field-of-view without introducing artificial sources. We alsoinclude a description of image processing steps to remove electronic artifactsspecific to Hawaii2-RG detectors like the one used for SEEDS, and a detailedanalysis of the Locally Optimized Combination of Images (LOCI) algorithmcommonly used to reduce high-contrast imaging data. ACORNS-ADI is written inpython. It is efficient and open-source, and includes several optional featureswhich may improve performance on data from other instruments. ACORNS-ADIrequires minimal modification to reduce data from instruments other thanHiCIAO. It is freely available for download atwww.github.com/t-brandt/acorns-adi under a BSD license.

New Techniques for High-Contrast Imaging with ADI: the ACORNS-ADI SEEDS Data Reduction Pipeline