# Spectral Sensitivity Estimation Without a Camera

arXiv: https://arxiv.org/abs/2304.11549 · HTML: https://mlsanity.com/paper/2304.11549/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 91 references that are in the map.
- First posted on arXiv 23 Apr 2023.

## Builds on (most cited first, 10 of 10)

- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [Cross-Camera Convolutional Color Constancy](https://mlsanity.com/paper/2011.11890/) (2020)
- [CIE XYZ Net: Unprocessing Images for Low-Level Computer Vision Tasks](https://mlsanity.com/paper/2006.12709/) (2020)
- [CLCC: Contrastive Learning for Color Constancy](https://mlsanity.com/paper/2106.04989/) (2021)
- [Time-Travel Rephotography](https://mlsanity.com/paper/2012.12261/) (2020)
- [NTIRE 2021 Depth Guided Image Relighting Challenge](https://mlsanity.com/paper/2104.13365/) (2021)
- [Learned Spectral Super-Resolution](https://mlsanity.com/paper/1703.09470/) (2017)
- [Semi-Supervised Raw-to-Raw Mapping](https://mlsanity.com/paper/2106.13883/) (2021)
- [Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report](https://mlsanity.com/paper/2210.11153/) (2022)
- [Standardized spectral and radiometric calibration of consumer cameras](https://mlsanity.com/paper/1906.04155/) (2019)

## Built on by (most cited first, 3 of 3)

- [Optimizing Illuminant Estimation in Dual-Exposure HDR Imaging](https://mlsanity.com/paper/2403.02449/) (2024)
- [Beyond Calibration: Physically Informed Learning for Raw-to-Raw Mapping](https://mlsanity.com/paper/2506.08650/) (2025)
- [Spectral Sensitivity Estimation with an Uncalibrated Diffraction Grating](https://mlsanity.com/paper/2508.00330/) (2025)

## Abstract (arXiv)

A number of problems in computer vision and related fields would be mitigated if camera spectral sensitivities were known. As consumer cameras are not designed for high-precision visual tasks, manufacturers do not disclose spectral sensitivities. Their estimation requires a costly optical setup, which triggered researchers to come up with numerous indirect methods that aim to lower cost and complexity by using color targets. However, the use of color targets gives rise to new complications that make the estimation more difficult, and consequently, there currently exists no simple, low-cost, robust go-to method for spectral sensitivity estimation. Furthermore, even if not limited by hardware or cost, researchers frequently work with imagery from multiple cameras that they do not have in their possession. To provide a practical solution to this problem, we propose a framework for spectral sensitivity estimation that not only does not require any hardware, but also does not require physical access to the camera itself. Similar to other work, we formulate an optimization problem that minimizes a two-term objective function: a camera-specific term from a system of equations, and a universal term that bounds the solution space. Different than other work, we use publicly available high-quality calibration data to construct both terms. We use the colorimetric mapping matrices provided by the Adobe DNG Converter to formulate the camera-specific system of equations, and constrain the solutions using an autoencoder trained on a database of ground-truth curves. On average, we achieve reconstruction errors as low as those that can arise due to manufacturing imperfections between two copies of the same camera. We provide our code and predicted sensitivities for 1,000+ cameras, and discuss which tasks can become trivial when camera responses are available.