Object Detection using Deep Convolutional Neural Network

Kumar, G.A.E. Satish; Sumalatha, R.

doi:10.1201/9781003338789-9

Cited by 2 publications

(1 citation statement)

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“…Grayscale image colorization is an active research area stimulated by the latest achievements in artificial intelligence (AI) techniques and the exciting applications of colored data in many domains, from medicine to entertainment. Colorized images have been proven to support several image processing tasks (e.g., object recognition and classification) [ 1 , 2 , 3 , 4 ], besides helping with diagnostics [ 5 , 6 , 7 ], the movie industry [ 8 ], and many other fields.…”

Section: Introductionmentioning

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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