“Auto-calibrated” parallel imaging (PI) strategies provide a robust mechanism for accelerating MRI acquisitions. However, most such strategies require the explicit formation of either coil sensitivity profiles or a cross-coil correlation operator, and as a result reconstruction corresponds to solving a challenging bilinear optimization problem. In this work, we present a novel image-domain approach to calibration-free parallel imaging (CPI) reconstruction called CLEAR. After reviewing contemporary parallel MRI strategies and overviewing CLEAR, we investigate its computational complexity and memory footprint, demonstrate its standalone application to undersampled Cartesian acquisitions, and discuss generalizations and future directions of investigation.