Expected Results

I shall again divide this section to accommodate the two main stages of the project.

For the first stage of the project, the deliverables are renderings of a noisy dataset, it corresponding de-noised version, and its corresponding analytical (noise-free) counterpart. I shall produce renderings for a pair of noisy CC and BCC datasets sampled from the same analytical function. I expect that it should be easy to visually verify the correctness of the noise models and their corresponding de-noising filters.

For the second stage of the project, the deliverables are plots as described in the Description section. Four possible results may be expected:

• The CC grid is better than the BCC grid in de-noising

• The CC grid is comparable to the BCC grid in de-noising

• The BCC grid is better than the CC grid in de-noising

• Something goes wrong, and no conclusions can be drawn

Of the four possible results, the third seems most likely. In my preliminary analysis, and stated loosely, given any radius of a continuous spherical de-noising filter, the filter will always cover more information on the BCC grid than on the CC grid. Since the filter has more information to work with on the BCC grid, the de-noised BCC dataset should contain less error due to noise compared to its CC counterpart. Therefore, the BCC grid should be better than the CC grid in de-noising.