Saliency mAP-Aided Generative Adversarial Network for RAW to RGB Mapping
Abstract
RAW files are widely applied in cameras and scanners as storage because they contain original optical data. Different cameras usually process the RAW files using diverse algorithms that are incompatible. To address the issue, we propose a general transformation method for cross-camera RAW to RGB mapping based on Generative Adversarial Network (GAN). Moreover, we propose a saliency map-aided data augmentation technique and the saliency maps are produced by Saliency GAN (SalGAN). Given RAW file as an input, it jointly predicts the RGB image and corresponding saliency map to enhance perceptual quality in the generated image. The proposed architecture is trained on the Zurich RAW2RGB (ZRR) dataset. Experimental results show that our method can generate more clear and visually plausible images than state-of-the-art networks.
Cite
Text
Zhao et al. "Saliency mAP-Aided Generative Adversarial Network for RAW to RGB Mapping." IEEE/CVF International Conference on Computer Vision Workshops, 2019. doi:10.1109/ICCVW.2019.00428Markdown
[Zhao et al. "Saliency mAP-Aided Generative Adversarial Network for RAW to RGB Mapping." IEEE/CVF International Conference on Computer Vision Workshops, 2019.](https://mlanthology.org/iccvw/2019/zhao2019iccvw-saliency/) doi:10.1109/ICCVW.2019.00428BibTeX
@inproceedings{zhao2019iccvw-saliency,
title = {{Saliency mAP-Aided Generative Adversarial Network for RAW to RGB Mapping}},
author = {Zhao, Yuzhi and Zhou, Chang and Yu, Wing Yin and Po, Lai-Man and Zhang, Tiantian and Liao, Zongbang and Shi, Xiang and Zhang, Yujia and Ou, Weifeng and Xian, Pengfei and Xiong, Jingjing},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2019},
pages = {3449-3457},
doi = {10.1109/ICCVW.2019.00428},
url = {https://mlanthology.org/iccvw/2019/zhao2019iccvw-saliency/}
}