Projects per year
Abstract
By placing a color filter in front of a camera we make new spectral sensitivities. The Luther-condition optimization solves for a color filter so that the camera’s filtered sensitivities are as close to being linearly related to the XYZ color matching functions (CMFs) as possible, that is, a filter is found that makes the camera more colorimetric. Arguably, the more general Vora-Value approach solves for the filter that best matches all possible target spectral sensitivity sets (e.g., any linear combination of the XYZ CMFs). A concern that we investigate here is that the filters found by the Luther and Vora-Value optimizations are different from one another. In this paper, we unify the Luther and Vora-Value approaches to prefilter design. We prove that if the target of the Luther-condition optimization is an orthonormal basis—a special linear combination of the XYZ CMFs which are orthogonal and are in unit length—the discovered Luther-filter is also the filter that maximizes the Vora-Value. A key advantage of using the Luther-condition formulation to maximize the Vora-Value is that it is both simpler to implement and converges to its optimal answer more quickly. Experiments validate our method.
| Original language | English |
|---|---|
| Article number | 6882 |
| Journal | Sensors |
| Volume | 20 |
| Issue number | 23 |
| DOIs | |
| Publication status | Published - 2 Dec 2020 |
Keywords
- Camera sensors
- Colorimetry
- Filter design
- Optimization method
Profiles
-
Graham Finlayson
- School of Computing Sciences - Professor of Computing Science
- Colour and Imaging Lab - Group Lead
Person: Research Group Member, Academic, Teaching and Research
Projects
- 1 Finished
-
Established Career Fellowship
Finlayson, G. (Principal Investigator) & Trollope, P. (Technician)
Engineering and Physical Sciences Research Council
1/09/19 → 30/06/25
Project: Fellowship
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