CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 8542 > Article
Paper
19 November 2012 Evaluation of image deblurring methods via a classification metric
Daniele Perrone, David Humphreys, Robert A. Lamb, Paolo Favaro
Author Affiliations +
Proceedings Volume 8542, Electro-Optical Remote Sensing, Photonic Technologies, and Applications VI; 854215 (2012) https://doi.org/10.1117/12.979199
Event: SPIE Security + Defence, 2012, Edinburgh, United Kingdom
Abstract
The performance of single image deblurring algorithms is typically evaluated via a certain discrepancy measure between the reconstructed image and the ideal sharp image. The choice of metric, however, has been a source of debate and has also led to alternative metrics based on human visual perception. While fixed metrics may fail to capture some small but visible artifacts, perception-based metrics may favor reconstructions with artifacts that are visually pleasant. To overcome these limitations, we propose to assess the quality of reconstructed images via a task-driven metric. In this paper we consider object classification as the task and therefore use the rate of classification as the metric to measure deblurring performance. In our evaluation we use data with different types of blur in two cases: Optical Character Recognition (OCR), where the goal is to recognise characters in a black and white image, and object classification with no restrictions on pose, illumination and orientation. Finally, we show how off-the-shelf classification algorithms benefit from working with deblurred images.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Daniele Perrone, David Humphreys, Robert A. Lamb, and Paolo Favaro "Evaluation of image deblurring methods via a classification metric", Proc. SPIE 8542, Electro-Optical Remote Sensing, Photonic Technologies, and Applications VI, 854215 (19 November 2012); https://doi.org/10.1117/12.979199
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 8 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Image quality
Optical character recognition
Detection and tracking algorithms
Image restoration
Visualization
Machine vision
Computer vision technology
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top