Image Colorization with Deep Convolutional Neural Networks

Pahal, Sudesh; Sehrawat, Preeti

doi:10.1007/978-981-15-5341-7_4

Cited by 3 publications

(1 citation statement)

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“…During the training process, CNNs learn the values of these filters. In recent years, these learning-based techniques have been widely adopted for solving the image colorization problem [ 22 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ]. The implemented solutions mainly differ for the employed network architectures and loss functions.…”

Section: Related Workmentioning

confidence: 99%

Colorizing the Past: Deep Learning for the Automatic Colorization of Historical Aerial Images

Farella¹,

Malek²,

Remondino³

2022

J. Imaging

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The colorization of grayscale images can, nowadays, take advantage of recent progress and the automation of deep-learning techniques. From the media industry to medical or geospatial applications, image colorization is an attractive and investigated image processing practice, and it is also helpful for revitalizing historical photographs. After exploring some of the existing fully automatic learning methods, the article presents a new neural network architecture, Hyper-U-NET, which combines a U-NET-like architecture and HyperConnections to handle the colorization of historical black and white aerial images. The training dataset (about 10,000 colored aerial image patches) and the realized neural network are available on our GitHub page to boost further research investigations in this field.

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