conMR

conMR refers to a class of algorithms designed for compressed sensing and related signal recovery problems. These methods typically operate by iteratively refining an estimate of the sparse signal being reconstructed. The core idea behind compressed sensing is that if a signal is sparse in some domain, it can be accurately recovered from a much smaller number of measurements than traditionally required by the Nyquist-Shannon sampling theorem. conMR algorithms aim to leverage this sparsity.

The "MR" in conMR often stands for "Matching Pursuit," a family of greedy algorithms that iteratively select the best atom (or basis vector) from a given dictionary to approximate the signal. conMR algorithms build upon this by incorporating elements of compressed sensing, specifically by dealing with a measurement matrix that is not necessarily orthogonal. They often involve a process of projecting the current estimate onto the measurement space, identifying discrepancies, and then updating the estimate in a way that promotes sparsity.

These iterative approaches can be computationally intensive but are effective in recovering signals from incomplete or noisy data. Variations of conMR algorithms exist, differing in their specific update rules, sparsity promoting functions, and how they handle noise. They find applications in various fields, including medical imaging, signal processing, and machine learning, where efficient recovery of sparse signals from limited data is crucial. The development of conMR algorithms reflects the ongoing research in bridging the gap between signal acquisition and reconstruction in undersampled scenarios.