Sparse signal representation and its applications in ultrasonic NDE

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Abstract

Highlights

★ Address recent accomplishments of SSR for processing ultrasonic NDE signals. ★ Explore in depth the advantages and limitations of various SSR algorithms. ★ Design of overcomplete dictionaries for processing ultrasonic NDE signals. ★ Investigate the performance of SSR compared to traditional methods. ★ Discuss the challenging issues met in practical ultrasonic NDE applications.

Many sparse signal representation (SSR) algorithms have been developed in the past decade. The advantages of SSR such as compact representations and super resolution lead to the state of the art performance of SSR for processing ultrasonic non-destructive evaluation (NDE) signals. Choosing a suitable SSR algorithm and designing an appropriate overcomplete dictionary is a key for success. After a brief review of sparse signal representation methods and the design of overcomplete dictionaries, this paper addresses the recent accomplishments of SSR for processing ultrasonic NDE signals. The advantages and limitations of SSR algorithms and various overcomplete dictionaries widely-used in ultrasonic NDE applications are explored in depth. Their performance improvement compared to conventional signal processing methods in many applications such as ultrasonic flaw detection and noise suppression, echo separation and echo estimation, and ultrasonic imaging is investigated. The challenging issues met in practical ultrasonic NDE applications for example the design of a good dictionary are discussed. Representative experimental results are presented for demonstration.

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