@inproceedings{6680b28b57274efdb4dfec9c064c59c3,
title = "Laplacian Pyramid Based Convolutional Neural Network for Multi-Exposure Fusion",
abstract = "Multi-exposure fusion (MEF) fuses a bracket of differently exposed low dynamic range images into one high-quality image. Motivated by the classical pyramid based MEF, a Laplacian pyramid based convolutional neural network (CNN) is proposed in this paper to fuse LDR images. The network integrates the multi-resolution fusion and non-linear inference of CNN in a model, maintaining global contrast and the detail in the fusion results. With a coarse-to-fine strategy, we rebuild the results from low-resolution to high-resolution, adding details to coarse fusion results progressively. The proposed network preserves details better than traditional CNN based MEF networks.",
keywords = "CNN, Coarse-to-Fine, Laplacian Pyramid, Multi-exposure Fusion, Residual Learning",
author = "Yilun Xu and Xingming Wu and Jianhua Wang and Hui Dong and Qiantong Wang and Haosong Yue and Weihai Chen",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 33rd Chinese Control and Decision Conference, CCDC 2021 ; Conference date: 22-05-2021 Through 24-05-2021",
year = "2021",
doi = "10.1109/CCDC52312.2021.9602586",
language = "英语",
series = "Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3555--3559",
booktitle = "Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021",
address = "美国",
}