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Image Colorization with Deep Convolutional Neural Networks

Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 668)

Abstract

Colorization, a task of coloring monochrome images or videos, plays an important role in the human perception of visual information, to black and white pictures or videos. Colorizing, when done manually in Photoshop, a single picture might take months to get exactly correct. Understanding the tediousness of the task and inspired by the benefits of artificial intelligence, we propose a mechanism to automate the coloring process with the help of convolutional neural networks (CNNs). Firstly, an Alpha version is developed which successfully works on trained images but fails to colorize images, and the network has never seen before. Subsequently, a Beta version is implemented which is able to overcome the limitations of Alpha version and works well for untrained images. To further enhance the network, we fused the deep CNN with a classifier called Inception ResNet V2 which is a pre-trained model. Finally, the training results are observed for all the versions followed by a comparative analysis for trained and untrained images.

Keywords

  • Colorization
  • Image classification
  • Convolutional neural networks
  • Resnet
  • Keras

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Correspondence to Sudesh Pahal .

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Pahal, S., Sehrawat, P. (2021). Image Colorization with Deep Convolutional Neural Networks. In: Hura, G., Singh, A., Siong Hoe, L. (eds) Advances in Communication and Computational Technology. Lecture Notes in Electrical Engineering, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-15-5341-7_4

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  • DOI: https://doi.org/10.1007/978-981-15-5341-7_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5340-0

  • Online ISBN: 978-981-15-5341-7

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