In this work, image processing algorithms are presented for an advanced sensor classification known collectively as imaging modulated polarimetry. The image processing algorithms presented are novel in that they use frequency domain based approaches, in comparison to the data domain based approaches that all previous algorithms have employed. Under the conditions on the data and imaging device derived in this work, the frequency domain based demodulation algorithms will optimally reduced reconstruction artifacts in a least squared sense. This work provides a framework for objectively comparing polarimeters that modulate in different domains (i.e. time vs. space), referred to as the spectral density response function. The spectral density response function is created as an analog to the modulation transfer function (or the more general transfer function for temporal devices) employed in the design of conventional imaging devices. The framework considers the total bandwidth of the object to be measured, and then can consider estimation artifacts that arise in both time and space due to the measurement modality that has been chosen. Using the framework for objectively comparing different modulated polarimeters (known as the spectral density response function), a method of developing a Wiener filter for multi-signal demodulation is developed, referred to as the polarimetric Wiener filter. This filter is then shown to be optimal for one extensive test case. This document provides one extensive example of implementing the algorithms and spectral density response calculations on a real system, known as the MSPI polarimeter. The MSPI polarimeter has been published extensively elsewhere, so only a basic system description here is used as necessary to describe how the methods presented here can be implemented on this system.