Notice of Violation of IEEE Publication Principles: Automatization of Coloring Grayscale Images Using Convolutional Neural Network

An approach based on deep learning for automatic colorization of image with optional user-guided hints. The system maps a gray-scale image, along with, user hints” (selected colors) to an output colorization with a Convolution Neural Network (CNN). Previous approaches have relied heavily on user input which results in non-real-time desaturated outputs. The network takes user edits by fusing low-level information of source with high-level information, learned from large-scale data. Some networks are trained on a large data set to eliminate this dependency. The image colorization systems find their applications in astronomical photography, CCTV footage, electron microscopy, etc. The various approaches combine color data from large data sets and user inputs provide a model for accurate and efficient colorization of grey-scale images.

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EIT-CDAE: A 2-D Electrical Impedance Tomography Image Reconstruction Method Based on Auto Encoder Technique

Gao

et al. 2019

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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Notice of Violation of IEEE Publication Principles: Automatization of Coloring Grayscale Images Using Convolutional Neural Network

Cited by 4 publications

References 6 publications

Convolution Neural Network and Auto-encoder Hybrid Scheme for Automatic Colorization of Grayscale Images

Convolution Neural Network and Auto-encoder Hybrid Scheme for Automatic Colorization of Grayscale Images

A Survey On Auto-Image Colorization Using Deep Learning Techniques With User Proposition

EIT-CDAE: A 2-D Electrical Impedance Tomography Image Reconstruction Method Based on Auto Encoder Technique

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