SAR Image Denoising Using Non-Local Means on MapReduce

Name
Jevgeni V├Ássotski
Abstract
Satellite systems designed for exploratory surface scanning face the problem of noise presence in images acquired electromagnetically, i.e by means of radars. A solution to this inherent problem has been searched for in the area of noise reduction filters applicable after the raw data is collected. The filtering algorithm Non-Local Means had shown to give good refinement results. However, the method is known to be computationally expensive, which poses a problem for processing of large datasets. In this work the parallel computing approach to this task was implemented on the distributed processing framework Apache Hadoop. It was shown that the Non-Local Means approach to noise reduction problem can be successfully adapted for execution in the distributed fashion of MapReduce model. Benchmark experiments were carried out on the test image to evaluate scalability of the approach. Tests confirmed high efficiency of parallelization (16 executor setup had given a speedup of 13.14x) and showed positive potential of Hadoop as a platform for massive image processing.
Graduation Thesis language
English
Graduation Thesis type
Bachelor - Computer Science
Supervisor(s)
Pelle Jakovits
Defence year
2014
 
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