Population coding is a coding scheme used in neural systems and is of general importance. It is ubiquitous in neurological systems. For this reason there is great interest in exploiting population coding in pattern recognition algorithms. A population of neural activities represents not only the value of some variable in the environment, but a full probability distribution for that variable. The information is held in a distributed and encoded form, which may in some situations be more robust to noise, and failures than conventional representations. Encoding a population code with discrete-valued elements (for example with the firing rate of a neuron) creates inaccuracies in the coded distributions. The result of these errors is the introduction of spurious high-frequency noise in the final distribution. We develop two methods of eliminating these errors and present results comparing the reconstruction accuracy of these techniques.