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PSF estimation using sharp edge prediction

Joshi, Neel, Szeliski, Richard, Kriegman, David J.·2008·2008 IEEE Conference on Computer Vision and Pattern Recognition

Abstract

Image blur is caused by a number of factors such as motion, defocus, capturing light over the non-zero area of the aperture and pixel, the presence of anti-aliasing filters on a camera sensor, and limited sensor resolution. We present an algorithm that estimates non-parametric, spatially-varying blur functions (i.e., point-spread functions or PSFs) at subpixel resolution from a single image. Our method handles blur due to defocus, slight camera motion, and inherent aspects of the imaging system. Our algorithm can be used to measure blur due to limited sensor resolution by estimating a sub-pixel, super-resolved PSF even for in-focus images. It operates by predicting a ldquosharprdquo version of a blurry input image and uses the two images to solve for a PSF. We handle the cases where the scene content is unknown and also where a known printed calibration target is placed in the scene. Our method is completely automatic, fast, and produces accurate results.

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Metadata

Authors
Joshi, N., Szeliski, R., & Kriegman, D. J.
Venue
2008 IEEE Conference on Computer Vision and Pattern Recognition
Publisher
IEEE
Published
2008-06
Pages
1-8
ISSN
1063-6919 (ISSN-L)
OpenAlex
W2135238609
Cited by
563
References
0
FWCI
21.10
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: PSF estimation using sharp edge prediction. https://app.cochat.ai/papers/10.1109/cvpr.2008.4587834
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Joshi, N., Szeliski, R., & Kriegman, D. J. (2008). PSF estimation using sharp edge prediction. 2008 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2008.4587834

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