Paper Info
Title
Multiframe super-resolution reconstruction of small moving objects
Abstract
Multiframe super-resolution (SR) reconstruction of small moving objects against a cluttered background is difficult for two reasons: a small object consists completely of "mixed" boundary pixels and the background contribution changes from frame-to-frame. We present a solution to this problem that greatly improves recognition of small moving objects under the assumption of a simple linear motion model in the real-world. The presented method not only explicitly models the image acquisition system, but also the space-time variant fore- and background contributions to the "mixed" pixels. The latter is due to a changing local background as a result of the apparent motion. The method simultaneously estimates a subpixel precise polygon boundary as well as a high-resolution (HR) intensity description of a small moving object subject to a modified total variation constraint. Experiments on simulated and real-world data show excellent performance of the proposed multiframe SR reconstruction method.
Year
DOI
Venue
2010
10.1109/TIP.2010.2068210
IEEE Transactions on Image Processing
Keywords
Field
DocType
image resolution,strontium,object recognition,total variation,noise,image reconstruction,image processing,space time,pixel,super resolution,data models,high resolution,image recognition
Iterative reconstruction,Computer vision,Linear motion,Polygon,Image processing,Artificial intelligence,Pixel,Subpixel rendering,Image resolution,Mathematics,Cognitive neuroscience of visual object recognition
Journal
Volume
Issue
ISSN
19
11
1057-7149
Citations 
PageRank 
References 
15
0.89
18
Authors
3
Name
Order
Citations
PageRank
Adam W. M. van Eekeren1322.53
Klamer Schutte217318.26
Lucas J. van Vliet3842113.16