Abstract
Colour images tend to be more visually appealing to humans compared to grayscale images, as colour images are closer in representation to the natural way we perceive our environment. While obtaining grayscale images from colour images is relatively trivial, the reverse process is not. In this paper, a machine learning method inspired by the Bayer filter and the demosaicing process of digital colour cameras, is proposed for the colourisation of grayscale images. The method involves training a multilayer perceptron model on colour images that are semantically similar to each other. The model can, henceforth, colourise grayscale images that are semantically similar to those in the training set. The success of our method is dependent on an image data representation model developed for this purpose. The proposed model gives impressive results despite requiring no human intervention and less machine resources for training when compared with existing deep learning models.
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Shokunbi, O.M., Akinyemi, J.D., Onifade, O.F.W. (2023). A Deep Multi-Layer Perceptron Model for Automatic Colourisation of Digital Grayscale Images. In: Chmielewski, L.J., Orłowski, A. (eds) Computer Vision and Graphics. ICCVG 2022. Lecture Notes in Networks and Systems, vol 598. Springer, Cham. https://doi.org/10.1007/978-3-031-22025-8_14
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DOI: https://doi.org/10.1007/978-3-031-22025-8_14
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