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Automated Blur Detection and Removal in Airborne Imaging Systems Using Imu Data : Volume Xxxix-b1, Issue 1 (24/07/2012)

By Shah, C. A.

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Book Id: WPLBN0004016379
Format Type: PDF Article :
File Size: Pages 3
Reproduction Date: 2015

Title: Automated Blur Detection and Removal in Airborne Imaging Systems Using Imu Data : Volume Xxxix-b1, Issue 1 (24/07/2012)  
Author: Shah, C. A.
Volume: Vol. XXXIX-B1, Issue 1
Language: English
Subject: Science, Isprs, International
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus Publications
Publication Date:
Publisher: Copernicus Publications, Göttingen, Germany
Member Page: Copernicus Publications


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Schickler, W., & Shah, C. A. (2012). Automated Blur Detection and Removal in Airborne Imaging Systems Using Imu Data : Volume Xxxix-b1, Issue 1 (24/07/2012). Retrieved from

Description: Microsoft Corporation, Bing Imagery Technologies, R&D, Boulder, CO 80305, USA. Images acquired by airborne sensors exhibit blur due to turbulent motion experienced by the aircraft. Significant amount of blur renders the images unusable for subsequent visual/automated analysis, requiring a re-flight. This necessitates quantifying the amount of blur in an image. Most approaches to blur quantification use image based methods to estimate the MTF (Modular Transfer Function) that indicates the amount of blur. Their limitations are – (1) MTF calculation requires presence of straight edges in the image scene, which may not always be available, (2) Due to the absence of an ideal edge, the amount of blur estimated is relative, and (3) It is computationally expensive and therefore may not be practical for blur detection in real time applications. Our solution uses the sensor motion as measured by an Inertial Measurement Unit (IMU) mounted in the camera system to calculate the motion experienced by the aircraft and the sensor during the time the shutter was actually open. This motion information together with the blur detection algorithm presented in this paper can provide an accurate quantification of blur in pixel units. Once we identify the images that exceed a given blur threshold, we use a blur removal algorithm that uses the IMU data and a natural image prior to compute per-pixel, spatially-varying blur, and then de-convolves an image to produce de-blurred images.

The presented blur detection and removal methods are currently being used offline to quantify and remove the blur from images acquired by the UltraCam systems within the Global Ortho Program (Walcher, 2012), which generates ortho imagery for all of the continental US as well as Western Europe, from over 2.5 million images. Furthermore, the blur detection method will be incorporated in the camera software of all our operational and forthcoming UltraCam imaging systems for real-time blur quantification.



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