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Statisticians, as well as machine learning and computer vision experts, havebeen studying image reconstitution through denoising different domains ofphotography, such as textual documentation, tomographic, astronomical, andlow-light photography. In this paper, we apply common inferential kernelfilters in the R and python languages, as well as Adobe Lightroom's denoisefilter, and compare their effectiveness in removing noise from JPEG images. Weran standard benchmark tests to evaluate each method's effectiveness forremoving noise. In doing so, we also surveyed students at Elon University abouttheir opinion of a single filtered photo from a collection of photos processedby the various filter methods. Many scientists believe that noise filters causeblurring and image quality loss so we analyzed whether or not people felt asthough denoising causes any quality loss as compared to their noiseless images.Individuals assigned scores indicating the image quality of a denoised photocompared to its noiseless counterpart on a 1 to 10 scale. Survey scores arecompared across filters to evaluate whether there were significant differencesin image quality scores received. Benchmark scores were compared to the visualperception scores. Then, an analysis of covariance test was run to identifywhether or not survey training scores explained any unplanned variation invisual scores assigned by students across the filter methods.

A Survey on the Visual Perceptions of Gaussian Noise Filtering on Photography