Optical chemical
imaging has established itself as a valuable technique
for visualizing analyte distributions in 2D, notably in medical, biological,
and environmental applications. In particular for image acquisitions
on small scales between few millimeter to the micrometer range, as
well as in heterogeneous samples with steep analyte gradients, image
resolution is essential. When individual pixels are inspected, however,
image noise becomes a metric as relevant as image accuracy and precision,
and denoising filters are applied to preserve relevant information.
While denoising filters smooth the image noise, they can also lead
to a loss of spatial resolution and thus to a loss of relevant information
about analyte distributions. To investigate the trade-off between
image resolution and noise reduction for information preservation,
we studied the impact of random camera noise and noise due to incorrect
camera settings on oxygen optodes using the ratiometric imaging technique.
First, we estimated the noise amplification across the calibration
process using a Monte Carlo simulation for nonlinear fit models. We
demonstrated how initially marginal random camera noise results in
a significant standard deviation (SD) for oxygen concentration of
up to 2.73% air under anoxic conditions, although the measurement
was conducted under ideal conditions and over 270 thousand sample
pixels were considered during calibration. Second, we studied the
effect of the Gaussian denoising filter on a steep oxygen gradient
and investigated the impact when the smoothing filter is applied during
data processing. Finally, we demonstrated the effectiveness of a Savitzky-Golay
filter compared to the well-established Gaussian filter.