Abstract
This paper presents an automatic grayscale image colorization method using convolutional neural network. Besides the gray target image, the user doesn’t need to provide a reference color image nor manual guidance. First we train the convolutional neural network using residual connections based on the VGG-16 model. For colorization, a grayscale image is forwarded through the network and using the highest layer infers some color information, then it up-scales the color guess and adds in information from the next highest layer. Experimental results and user study on a large set of images demonstrate that our colorization method is competitive with previous state-of-the-art methods.
This paper has been retracted because parts of the work were copied from the following publication: “Automatic Colorization” in http://tinyclouds.org/colorize/. The erratum to this chapter is available at 10.1007/978-981-10-2260-9_28
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-981-10-2260-9_28
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Zhao, Y., Xu, D., Zhang, Y. (2016). Retracted Chapter: Image Colorization Using Convolutional Neural Network. In: Tan, T., et al. Advances in Image and Graphics Technologies. IGTA 2016. Communications in Computer and Information Science, vol 634. Springer, Singapore. https://doi.org/10.1007/978-981-10-2260-9_27
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DOI: https://doi.org/10.1007/978-981-10-2260-9_27
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