University of Oulu

Mustaniemi et el. (2020, 7th-10th September). LSD₂ – Joint Denoising and Deblurring of Short and Long Exposure Images with CNNs. The 31st British Machine Vision Virtual Conference. https://www.bmvc2020-conference.com/assets/papers/0725.pdf

LSD₂ – joint denoising and deblurring of short and long exposure images with CNNs

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Author: Mustaniemi, Janne1; Kannala, Juho2; Matas, Jiri3;
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu, Finland
2Department of Computer Science, Aalto University, Finland
3Center for Machine Perception, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic
4Department of Electrical Engineering and Automation, Aalto University, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 7.8 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020120198898
Language: English
Published: British Machine Vision Association, 2020
Publish Date: 2020-12-01
Description:

Abstract

The paper addresses the problem of acquiring high-quality photographs with handheld smartphone cameras in low-light imaging conditions. We propose an approach based on capturing pairs of short and long exposure images in rapid succession and fusing them into a single high-quality photograph. Unlike existing methods, we take advantage of both images simultaneously and perform a joint denoising and deblurring using a convolutional neural network. A novel approach is introduced to generate realistic short-long exposure image pairs. The method produces good images in extremely challenging conditions and outperforms existing denoising and deblurring methods. It also enables exposure fusion in the presence of motion blur.

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Conference: The 31st British Machine Vision Virtual Conference : 7th - 10th September 2020
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
Funding: The authors would like to thank Business Finland for the financial support of this research project (grant no. 1848/31/2015). J. Matas was supported by project CZ.02.1.01/0.0/0.0/16019/0000765 Research Center for Informatics.
Copyright information: © 2020. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.