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Learning based image transformation using convolutional neural networks

Hou, Xianxu; Gong, Yuanhao; Liu, Bozhi; Sun, Ke; Liu, Jingxin; Xu, Bolei; Duan, Jiang; Qiu, Guoping


Xianxu Hou

Yuanhao Gong

Bozhi Liu

Ke Sun

Jingxin Liu

Bolei Xu

Jiang Duan

Professor of Visual Information Processing


We have developed a learning-based image transformation framework and successfully applied it to three common image transformation operations: downscaling, decolorization, and high dynamic range image tone mapping. We use a convolutional neural network (CNN) as a non-linear mapping function to transform an input image to a desired output. A separate CNN network trained for a very large image classification task is used as a feature extractor to construct the training loss function of the image transformation CNN. Unlike similar applications in the related literature such as image super-resolution, none of the problems addressed in this paper have a known ground truth or target. For each problem, we reason about
a suitable learning objective function and develop an effective solution. This is the first work that uses deep learning to solve and unify these three common image processing tasks. We present experimental results to demonstrate the effectiveness of the new technique and its state-of-the-art performances.


Hou, X., Gong, Y., Liu, B., Sun, K., Liu, J., Xu, B., …Qiu, G. (2018). Learning based image transformation using convolutional neural networks. IEEE Access, 6, 49779-49792.

Journal Article Type Article
Acceptance Date Aug 28, 2018
Online Publication Date Sep 6, 2018
Publication Date Sep 28, 2018
Deposit Date Feb 1, 2019
Publicly Available Date Feb 1, 2019
Journal IEEE Access
Electronic ISSN 2169-3536
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 6
Pages 49779-49792
Keywords Deep learning; image downscaling; image decolorization; HDR image tone mapping
Public URL
Publisher URL


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