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Information Journal Paper

Title

Mapping Grayscale Images to Colour Space Using Deep Learning

Pages

  52-68

Keywords

Convolutional Neural Networks (CNN) 
RGB 
CIELAB (Lab) 

Abstract

 People are used to exploring grayscale images in their family albums but it is difficult to grasp the reality without colours. Luckily, with advancements in Machine Learning it has been possible to solve problems previously thought impossible. The authors aim to automatically colourize grayscale images using a subset of Machine Learning called Deep Learning. The system will be trained on an image dataset and given an input grayscale image the model will be able to assign aesthetically believable colours. A grayscale photograph has been provided; our approach solves the problem of visualizing a reasonable colour version of the grayscale picture. This issue is undoubtedly under controlled; therefore earlier methods to this problem have either counted majorly on user interaction or it leads to in unsaturated colourizations. The authors put forward a completely automatic approach that will try to produce realistic and vibrant colourizations as much as possible. The proposed system has been applied as a feed-forward in a Convolutional Neural Network and has been trained on over twenty thousand colour images currently.

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  • Cite

    APA: Copy

    Saini, Anu, & Tripathi, Jyoti. (2022). Mapping Grayscale Images to Colour Space Using Deep Learning. JOURNAL OF INFORMATION TECHNOLOGY MANAGEMENT, 14(supplement 2), 52-68. SID. https://sid.ir/paper/981282/en

    Vancouver: Copy

    Saini Anu, Tripathi Jyoti. Mapping Grayscale Images to Colour Space Using Deep Learning. JOURNAL OF INFORMATION TECHNOLOGY MANAGEMENT[Internet]. 2022;14(supplement 2):52-68. Available from: https://sid.ir/paper/981282/en

    IEEE: Copy

    Anu Saini, and Jyoti Tripathi, “Mapping Grayscale Images to Colour Space Using Deep Learning,” JOURNAL OF INFORMATION TECHNOLOGY MANAGEMENT, vol. 14, no. supplement 2, pp. 52–68, 2022, [Online]. Available: https://sid.ir/paper/981282/en

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