Mathematical framework for perception-driven parameter choice in image denoising
This work addresses the problem of parameter selection in image denoising for human visual perception, but the contribution is incremental as it combines existing methods (psychometric scaling, HaarPSI) without demonstrating a clear advantage over standard metrics.
The authors developed a mathematical framework to select denoising parameters that optimize perceived similarity for human observers, using psychometric scaling to derive a HaarPSI threshold for parameter discretization. They produced openly available image sets for perception-driven imaging research.
We approach image denoising from a perception-driven perspective: how can we select the parameters that are best suited for human visual perception? We combine research methods in mathematics and psychology to develop a mathematical framework for measuring perceived similarity. We construct a sample set of differently denoised photographs by using the same base image as input data and by tuning the parameter value in a total variation denoising algorithm. A comparison test is conducted with human participants to survey perceived differences between the images. Analyzing the results with psychometric scaling provides us with a HaarPSI value to use as a threshold in discretizing parameter grids. As a result, we obtain psychometrically scaled, openly available image sets that are ready to use in further experiments in perception-driven imaging, as well as a framework for ensuing experiments involving comparison tests.