Scheduled System Maintenance on May 29th, 2015:
IEEE Xplore will be upgraded between 11:00 AM and 10:00 PM EDT. During this time there may be intermittent impact on performance. We apologize for any inconvenience.
By Topic

Deblurring From Highly Incomplete Measurements for Remote Sensing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Jianwei Ma ; Sch. of Aerosp., Tsinghua Univ., Beijing ; Le Dimet, F.-X.

When we take photos, we often get blurred pictures because of hand shake, motion, insufficient light, unsuited focal length, or other disturbances. Recently, a compressed-sensing (CS) theorem which provides a new sampling theory for data acquisition has been applied for medical and astronomic imaging. The CS makes it possible to take superresolution photos using only one or a few pixels, rather than million pixels, with a conventional digital camera. Here, we further consider a so-called CS deblurring problem: Can we still obtain clear pictures from highly incomplete measurements when blurring disturbances occur? A decoding algorithm based on Poisson singular integral and iterative curvelet thresholding is proposed to correct the deblurring problem with surprisingly incomplete measurements. It permits one to design robust and practical compressed-imaging instruments involving less imaging time, less storage space, less power consumption, smaller size, and cheaper than currently used charged coupled device cameras, which effectively match the needs, particularly for probes sent very far away. It essentially shifts the onboard imaging cost to an offline recovery computational cost. Potential applications in aerospace remote sensing of the Chinese Chang'e-1 lunar probe are presented.

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:47 ,  Issue: 3 )