Pith. sign in

REVIEW 2 cited by

Sensor-Independent Illumination Estimation for DNN Models

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1912.06888 v1 pith:UYL52A6L submitted 2019-12-14 cs.CV

Mahmoud Afifi , Michael S. Brown This is my paper
classification cs.CV
keywords sensorcameraspaceestimationsensor-independentilluminantmodelnecessary
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

While modern deep neural networks (DNNs) achieve state-of-the-art results for illuminant estimation, it is currently necessary to train a separate DNN for each type of camera sensor. This means when a camera manufacturer uses a new sensor, it is necessary to retrain an existing DNN model with training images captured by the new sensor. This paper addresses this problem by introducing a novel sensor-independent illuminant estimation framework. Our method learns a sensor-independent working space that can be used to canonicalize the RGB values of any arbitrary camera sensor. Our learned space retains the linear property of the original sensor raw-RGB space and allows unseen camera sensors to be used on a single DNN model trained on this working space. We demonstrate the effectiveness of this approach on several different camera sensors and show it provides performance on par with state-of-the-art methods that were trained per sensor.

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes

    cs.CV 2026-01 unverdicted novelty 7.0 of 10

    RL-AWB uses reinforcement learning to optimize parameters of a statistical white-balance estimator for nighttime scenes and reports better generalization on a new multi-sensor dataset.

  2. RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes

    cs.CV 2026-01 conditional novelty 6.0 of 10

    RL-AWB uses a soft actor-critic RL agent to per-image tune a novel gray-pixel illuminant estimator, achieving competitive nighttime white balance with only five training images and better cross-sensor generalization t...

Pith tools