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2018 | OriginalPaper | Chapter

Multi-scale Convolutional Neural Networks for Non-blind Image Deconvolution

Authors : Xuehui Wang, Feng Dai, Jinli Suo, Yongdong Zhang, Qionghai Dai

Published in: Advances in Multimedia Information Processing – PCM 2017

Publisher: Springer International Publishing

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Abstract

Image deconvolution appears in many image-related problems. Previous works tried to train neural networks directly on blurry/clean pairs to restore clean images but failed. In this work, we propose a novel neural network, trained end-to-end, pixels-to-pixels, to deblur images from blurry ones. Our key insight is to build multi-scale convolutional neural networks that extract various scale feature maps which is essential for recovering sharp images and removing artifacts. The networks take input image of arbitrary size and produce output within efficient time. We demonstrate that our approach yields better result than the state-of-the-art deconvolution algorithms on a large dataset.

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previous chapter Anomaly Detection with Passive Aggressive Online Gaussian Model Estimation
next chapter Feature-Preserving Mesh Denoising Based on Guided Normal Filtering
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Metadata
Title
Multi-scale Convolutional Neural Networks for Non-blind Image Deconvolution
Authors
Xuehui Wang
Feng Dai
Jinli Suo
Yongdong Zhang
Qionghai Dai
Copyright Year
2018
Book
Advances in Multimedia Information Processing – PCM 2017

Print ISBN: 978-3-319-77382-7

Electronic ISBN: 978-3-319-77383-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-77383-4_89