  Relative colour constancy is an essential requirement for many scientific
imaging applications. However, most digital cameras differ in their image
formations and native sensor output is usually inaccessible, e.g., in
smartphone camera applications. This makes it hard to achieve consistent colour
assessment across a range of devices, and that undermines the performance of
computer vision algorithms. To resolve this issue, we propose a colour
alignment model that considers the camera image formation as a black-box and
formulates colour alignment as a three-step process: camera response
calibration, response linearisation, and colour matching. The proposed model
works with non-standard colour references, i.e., colour patches without knowing
the true colour values, by utilising a novel balance-of-linear-distances
feature. It is equivalent to determining the camera parameters through an
unsupervised process. It also works with a minimum number of corresponding
colour patches across the images to be colour aligned to deliver the applicable
processing. Two challenging image datasets collected by multiple cameras under
various illumination and exposure conditions were used to evaluate the model.
Performance benchmarks demonstrated that our model achieved superior
performance compared to other popular and state-of-the-art methods.
