  High dynamic range (HDR) imaging involves capturing a series of frames of the
same scene, each with different exposure settings, to broaden the dynamic range
of light. This can be achieved through burst capturing or using staggered HDR
sensors that capture long and short exposures simultaneously in the camera
image signal processor (ISP). Within camera ISP pipeline, illuminant estimation
is a crucial step aiming to estimate the color of the global illuminant in the
scene. This estimation is used in camera ISP white-balance module to remove
undesirable color cast in the final image. Despite the multiple frames captured
in the HDR pipeline, conventional illuminant estimation methods often rely only
on a single frame of the scene. In this paper, we explore leveraging
information from frames captured with different exposure times. Specifically,
we introduce a simple feature extracted from dual-exposure images to guide
illuminant estimators, referred to as the dual-exposure feature (DEF). To
validate the efficiency of DEF, we employed two illuminant estimators using the
proposed DEF: 1) a multilayer perceptron network (MLP), referred to as
exposure-based MLP (EMLP), and 2) a modified version of the convolutional color
constancy (CCC) to integrate our DEF, that we call ECCC. Both EMLP and ECCC
achieve promising results, in some cases surpassing prior methods that require
hundreds of thousands or millions of parameters, with only a few hundred
parameters for EMLP and a few thousand parameters for ECCC.
