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arXiv · 2606.00122

Mathematical framework for perception-driven parameter choice in image denoising

Saara Isoranta·Emilia L. K. Blåsten·Lílian Ferreira de Freitas·Jukka Häkkinen·Markus Juvonen·Samuli Siltanen
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Abstract

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.

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Saara Isoranta, Emilia L. K. Blåsten, Lílian Ferreira de Freitas, Jukka Häkkinen, Markus Juvonen, Samuli Siltanen. 2026-05-28. Mathematical framework for perception-driven parameter choice in image denoising. https://arxiv.org/abs/2606.00122

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