Broad Colorization

Yuxi Jin, Bin Sheng, Ping Li, C. L. Philip Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

The scribble-And example-based colorization methods have fastidious requirements for users, and the training process of deep neural networks for colorization is quite time-consuming. We instead proposed an automatic colorization approach with no dependence on user input and no need to endure long training time, which combines local features and global features of the input gray-scale images. Low-, mid-, and high-level features are united as local features representing cues existed in the gray-scale image. The global feature is regarded as data prior to guiding the colorization process. The local broad learning system is trained for getting the chrominance value of each pixel from the local features, which could be expressed as a chrominance map according to the position of pixels. Then, the global broad learning system is trained to refine the chrominance map. There are no requirements for users in our approach, and the training time of our framework is an order of magnitude faster than the traditional methods based on deep neural networks. To increase the user's subjective initiative, our system allows users to increase training data without retraining the system. Substantial experimental results have shown that our approach outperforms state-of-The-Art methods.

Original languageEnglish
Article number9132683
Pages (from-to)2330-2343
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume32
Issue number6
DOIs
Publication statusPublished - Jun 2021

Keywords

  • Colorization
  • global broad learning system (GBLS)
  • global features
  • local broad learning system (LBLS)
  • local features

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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