@inproceedings{913a9710205245cca75aec5ecdfcd86a,
title = "Kernel estimation for motion blur removal using deep convolutional neural network",
abstract = "Blind deblurring can restore the sharp image from the blur version when the blur kernel is unknown, which is a challenging task. Kernel estimation is crucial for blind deblurring. In this paper, a novel blur kernel estimation method based on regression model is proposed for motion blur. The motion blur features are firstly mined through convolutional neural network (CNN), and then mapped to motion length and orientation by support vector regression (SVR). Experiments show that the proposed model, namely CNNSVR, can give more accurate kernel estimation and generate better deblurring result compared with other state-of-the-art algorithms.",
keywords = "Blind deblurring, CNN, Deep learning, Kernel estimation, Motion blur",
author = "Yanan Lu and Fengying Xie and Zhiguo Jiang",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 24th IEEE International Conference on Image Processing, ICIP 2017 ; Conference date: 17-09-2017 Through 20-09-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/ICIP.2017.8296984",
language = "英语",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "3755--3759",
booktitle = "2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings",
address = "美国",
}