Revisiting and Optimising a CNN Colour Constancy Method for Multi-Illuminant Estimation

TL;DR

This paper introduces a CNN-based framework for multi-illuminant estimation in colour constancy, effectively handling scenes with multiple light sources and outperforming recent methods.

Contribution

A novel deep learning approach that estimates multiple scene illuminants and improves colour correction in complex lighting conditions.

Findings

Outperforms recent state-of-the-art methods

Effective in both multi- and single-illuminant scenarios

Produces promising visual results

Abstract

The aim of colour constancy is to discount the effect of the scene illumination from the image colours and restore the colours of the objects as captured under a 'white' illuminant. For the majority of colour constancy methods, the first step is to estimate the scene illuminant colour. Generally, it is assumed that the illumination is uniform in the scene. However, real world scenes have multiple illuminants, like sunlight and spot lights all together in one scene. We present in this paper a simple yet very effective framework using a deep CNN-based method to estimate and use multiple illuminants for colour constancy. Our approach works well in both the multi and single illuminant cases. The output of the CNN method is a region-wise estimate map of the scene which is smoothed and divided out from the image to perform colour constancy. The method that we propose outperforms other recent and state of the art methods and has promising visual results.