Performance comparison of classical methods and neural networks for colour correction

Kucuk, Abdullah, Finlayson, Graham D., Mantiuk, Rafal and Ashraf, Maliha (2023) Performance comparison of classical methods and neural networks for colour correction. Journal of Imaging, 9 (10). ISSN 2313-433X

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Colour correction is the process of converting RAW RGB pixel values of digital cameras to a standard colour space such as CIE XYZ. A range of regression methods including linear, polynomial and root-polynomial least-squares have been deployed. However, in recent years, various neural network (NN) models have also started to appear in the literature as an alternative to classical methods. In the first part of this paper, a leading neural network approach is compared and contrasted with regression methods. We find that, although the neural network model supports improved colour correction compared with simple least-squares regression, it performs less well than the more advanced root-polynomial regression. Moreover, the relative improvement afforded by NNs, compared to linear least-squares, is diminished when the regression methods are adapted to minimise a perceptual colour error. Problematically, unlike linear and root-polynomial regressions, the NN approach is tied to a fixed exposure (and when exposure changes, the afforded colour correction can be quite poor). We explore two solutions that make NNs more exposure-invariant. First, we use data augmentation to train the NN for a range of typical exposures and second, we propose a new NN architecture which, by construction, is exposure-invariant. Finally, we look into how the performance of these algorithms is influenced when models are trained and tested on different datasets. As expected, the performance of all methods drops when tested with completely different datasets. However, we noticed that the regression methods still outperform the NNs in terms of colour correction, even though the relative performance of the regression methods does change based on the train and test datasets.

Item Type: Article
Additional Information: Funding Information: This research was funded by Spectricity and EPSRC grant EP/S028730/1.
Uncontrolled Keywords: colour correction,exposure invariance,neural network,optimisation,polynomial,regression,radiology nuclear medicine and imaging,computer vision and pattern recognition,computer graphics and computer-aided design,electrical and electronic engineering ,/dk/atira/pure/subjectarea/asjc/2700/2741
Faculty \ School: Faculty of Science > School of Computing Sciences
UEA Research Groups: Faculty of Science > Research Groups > Colour and Imaging Lab
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 24 May 2024 14:30
Last Modified: 30 May 2024 16:31
DOI: 10.3390/jimaging9100214


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