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Erschienen in:
Background Modeling Using Temporal-Local Sample Density Outlier Detection Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Multi-exposure Image Fusion Using Propagated Image Filtering

verfasst von : Diptiben Patel, Bhoomika Sonane, Shanmuganathan Raman

Erschienen in: Proceedings of International Conference on Computer Vision and Image Processing

Verlag: Springer Singapore

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Abstract

Image fusion is the process of combining multiple images of a same scene to single high-quality image which has more information than any of the input images. In this paper, we propose a new fusion approach in a spatial domain using propagated image filter. The proposed approach calculates the weight map of every input image using the propagated image filter and gradient domain postprocessing. Propagated image filter exploits cumulative weight construction approach for filtering operation. We show that the proposed approach is able to achieve state-of-the-art results for the problem of multi-exposure fusion for various types of indoor and outdoor natural static scenes with varying amounts of dynamic range.

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Vorheriges Kapitel A Local Correlation and Directive Contrast Based Image Fusion
Nächstes Kapitel Tone Mapping HDR Images Using Local Texture and Brightness Measures
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Metadaten
Titel
Multi-exposure Image Fusion Using Propagated Image Filtering
verfasst von
Diptiben Patel
Bhoomika Sonane
Shanmuganathan Raman
Copyright-Jahr
2017
Verlag
Springer Singapore
Buch
Proceedings of International Conference on Computer Vision and Image Processing

Print ISBN: 978-981-10-2103-9

Electronic ISBN: 978-981-10-2104-6

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-981-10-2104-6_39

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