# Designing Color Filters that Make Cameras MoreColorimetric

arXiv: https://arxiv.org/abs/2003.12645 · HTML: https://mlsanity.com/paper/2003.12645/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2020.

## Built on by (most cited first, 7 of 7)

- [Rawformer: Unpaired Raw-to-Raw Translation for Learnable Camera ISPs](https://mlsanity.com/paper/2404.10700/) (2024)
- [CCMNet: Leveraging Calibrated Color Correction Matrices for Cross-Camera Color Constancy](https://mlsanity.com/paper/2504.07959/) (2025)
- [Joint RGB-Spectral Decomposition Model Guided Image Enhancement in Mobile Photography](https://mlsanity.com/paper/2407.17996/) (2024)
- [Matched Illumination](https://mlsanity.com/paper/2201.11700/) (2022)
- [Modeling spectral filtering effects on color-matching functions: Implications for observer variability](https://mlsanity.com/paper/2508.19291/) (2025)
- [Towards Imperceptible Watermarking Via Environment Illumination for Consumer Cameras](https://mlsanity.com/paper/2510.17114/) (2025)
- [Illuminant-Adaptive 3D Lookup Tables for Camera Color Correction](https://mlsanity.com/paper/2607.11681/) (2026)

## Abstract (arXiv)

When we place a colored filter in front of a camera the effective camera response functions are equal to the given camera spectral sensitivities multiplied by the filter spectral transmittance. In this paper, we solve for the filter which returns the modified sensitivities as close to being a linear transformation from the color matching functions of human visual system as possible. When this linearity condition - sometimes called the Luther condition - is approximately met, the `camera+filter' system can be used for accurate color measurement. Then, we reformulate our filter design optimisation for making the sensor responses as close to the CIEXYZ tristimulus values as possible given the knowledge of real measured surfaces and illuminants spectra data. This data-driven method in turn is extended to incorporate constraints on the filter (smoothness and bounded transmission). Also, because how the optimisation is initialised is shown to impact on the performance of the solved-for filters, a multi-initialisation optimisation is developed. Experiments demonstrate that, by taking pictures through our optimised color filters we can make cameras significantly more colorimetric.