Mathematical framework for perception-driven parameter choice in image denoising
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914619404058624 |
|---|---|
| author | Isoranta, Saara Blåsten, Emilia L. K. de Freitas, Lílian Ferreira Häkkinen, Jukka Juvonen, Markus Siltanen, Samuli |
| author_facet | Isoranta, Saara Blåsten, Emilia L. K. de Freitas, Lílian Ferreira Häkkinen, Jukka Juvonen, Markus Siltanen, Samuli |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_00122 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Mathematical framework for perception-driven parameter choice in image denoising Isoranta, Saara Blåsten, Emilia L. K. de Freitas, Lílian Ferreira Häkkinen, Jukka Juvonen, Markus Siltanen, Samuli Image and Video Processing Numerical Analysis 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. |
| title | Mathematical framework for perception-driven parameter choice in image denoising |
| topic | Image and Video Processing Numerical Analysis |
| url | https://arxiv.org/abs/2606.00122 |