Content-Preserving Tone Adjustment for Image Enhancement

Simone Bianco, Claudio Cusano, Flavio Piccoli, Raimondo Schettini; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract


We propose a novel method based on Convolutional Neural Networks for content-preserving tone adjustment. The method is at the same time fast and accurate since we decouple the inference of the parameters and the color transform: the parameters are inferred from a downsampled version of the input image and the transformation is applied to the full resolution input. The method includes two steps of image enhancement: the first one is a global color transformation, while the second one is a local transformation. Experiments conducted on the DPED - DSLR Photo Enhancement Dataset, that has been used for the NTIRE19 Image Enhancement Challenge, and on the MIT-Adobe FiveK dataset, that is widely used for image enhancement, demonstrate the effectiveness of the proposed method.

Related Material


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[bibtex]
@InProceedings{Bianco_2019_CVPR_Workshops,
author = {Bianco, Simone and Cusano, Claudio and Piccoli, Flavio and Schettini, Raimondo},
title = {Content-Preserving Tone Adjustment for Image Enhancement},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}