Image Colorization Algorithm based on Dense Neural Network

Volume 15, Number 1, January 2019, pp. 270-280
DOI: 10.23940/ijpe.19.01.p27.270280

Pinle Qin, Na Zhang, Jianchao Zeng, and Yulong Song

Abstract:

In most scenes, color images have richer information than grayscale images. This paper presents a method of grayscale image pseudo coloring that constructed and trained an end-to-end deep learning model based on dense neural network aims to extract all kinds of information and features (such as classification information and detail feature information). Entering a grayscale picture to the trained network can generate a full and vibrant vivid color picture. By constantly training the entire network on a wide variety of data sets, you will get the most adaptable, high-performance pseudo color network. The experiments show that the method proposed has a higher utilization of features and can obtain a satisfactory coloring effect. Compared with the current advanced pseudo color methods, it has also made remarkable improvements, and to a certain extent, the problem during the coloring processing have been improved, such as color overflow, loss of details, low contrast etc.

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