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A Survey of Blur Detection and Sharpness Assessment MethodsOut-of-Focus Blur

A Survey of Blur Detection and Sharpness Assessment Methods: Out-of-Focus Blur [Natural images usually include defocus blur due to the existence of objects at different depths from the camera. The depth richness of a scene translates into a spatially variable defocus blur in the captured image which cannot be easily undone with image deconvolution algorithms not only due to their computational requirements but also because most of the blind deconvolution algorithms assume a spatially invariant blur [20]. Automatic blur detection is an important element for several computer vision tasks such as spatially varying deblurring [21], photo editing [22], image classification [23], depth estimation [24], saliency detection [18], image segmentation [25], and digital image forensic analysis [26].] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Survey of Blur Detection and Sharpness Assessment MethodsOut-of-Focus Blur

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Publisher
Springer International Publishing
Copyright
© Springer Nature Switzerland AG 2021
ISBN
978-3-031-00401-8
Pages
7 –43
DOI
10.1007/978-3-031-01529-8_2
Publisher site
See Chapter on Publisher Site

Abstract

[Natural images usually include defocus blur due to the existence of objects at different depths from the camera. The depth richness of a scene translates into a spatially variable defocus blur in the captured image which cannot be easily undone with image deconvolution algorithms not only due to their computational requirements but also because most of the blind deconvolution algorithms assume a spatially invariant blur [20]. Automatic blur detection is an important element for several computer vision tasks such as spatially varying deblurring [21], photo editing [22], image classification [23], depth estimation [24], saliency detection [18], image segmentation [25], and digital image forensic analysis [26].]

Published: Jan 1, 2021

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