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

2017 | OriginalPaper | Buchkapitel

Fast Non-blind Image Deblurring with Sparse Priors

verfasst von : Rajshekhar Das, Anurag Bajpai, Shankar M. Venkatesan

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

Verlag: Springer Singapore

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Abstract

Capturing clear images in dim light conditions remains a critical problem in digital photography. Long exposure time inevitably leads to motion blur due to camera shake. On the other hand, short exposure time with high gain yields sharp but noisy images. However, exploiting information from both the blurry and noisy images can produce superior results in image reconstruction. In this paper, we employ the image pairs to carry out a non-blind deconvolution and compare the performances of three different deconvolution methods, namely, Richardson Lucy algorithm, Algebraic deconvolution, and Basis Pursuit deconvolution. We show that the Basis Pursuit approach produces the best results in most cases.

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Vorheriges Kapitel Integrating Geometric and Textural Features for Facial Emotion Classification Using SVM Frameworks
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Metadaten
Titel
Fast Non-blind Image Deblurring with Sparse Priors
verfasst von
Rajshekhar Das
Anurag Bajpai
Shankar M. Venkatesan
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_56

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