@inproceedings{82d1df2390184696966497b786a8cde9,
title = "Uneven illumination removal based on fully convolutional network for dermoscopy images",
abstract = "For the dermoscopy image, uneven illumination will influence segmentation accuracy and lead to wrong aided diagnosis result. In this paper, an uneven illumination removal method based on deep learning is proposed for dermoscopy images. Different from the traditional Retinex based methods, which estimate the illumination component using statistical methods to obtain the reflectance component of the image (uneven illumination removal result), in this paper, the illumination component is regarded as a black box to be learned by a designed fully convolutional neural network(FCN) model. The designed FCN model has more scales and can mine more effective features to obtain good illumination correction results. Experiment results show that, compared with 7 other state-of-art algorithms, our method can remove uneven illumination more effectively, and with our method, the segmentation performance is improved greatly.",
keywords = "deep learning, dermoscopy images, fully convolutional network, Retinex, Uneven illumination",
author = "Mei, \{Xiao Feng\} and Xie, \{Feng Ying\} and Jiang, \{Zhi Guo\}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 13th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2017 ; Conference date: 16-12-2016 Through 18-12-2016",
year = "2017",
month = oct,
day = "20",
doi = "10.1109/ICCWAMTIP.2016.8079847",
language = "英语",
series = "2016 13th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "243--247",
booktitle = "2016 13th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2017",
address = "美国",
}