  Statisticians, as well as machine learning and computer vision experts, have
been studying image reconstitution through denoising different domains of
photography, such as textual documentation, tomographic, astronomical, and
low-light photography. In this paper, we apply common inferential kernel
filters in the R and python languages, as well as Adobe Lightroom's denoise
filter, and compare their effectiveness in removing noise from JPEG images. We
ran standard benchmark tests to evaluate each method's effectiveness for
removing noise. In doing so, we also surveyed students at Elon University about
their opinion of a single filtered photo from a collection of photos processed
by the various filter methods. Many scientists believe that noise filters cause
blurring and image quality loss so we analyzed whether or not people felt as
though denoising causes any quality loss as compared to their noiseless images.
Individuals assigned scores indicating the image quality of a denoised photo
compared to its noiseless counterpart on a 1 to 10 scale. Survey scores are
compared across filters to evaluate whether there were significant differences
in image quality scores received. Benchmark scores were compared to the visual
perception scores. Then, an analysis of covariance test was run to identify
whether or not survey training scores explained any unplanned variation in
visual scores assigned by students across the filter methods.
