Location

Brunswick, ME

Start Date

1-1-1997 12:00 AM

Description

Ultrasonic NDE images are often contaminated with speckle noise. The degradation caused by the presence of speckle noise makes it difficult to identify features of interest that are typically thin or small in nature. A variety of techniques have been proposed to date for reducing such noise. As an example, lowpass filters can be employed to reduce speckle noise. However, they tend to blur thin features and edges. Median filters are also used widely to remove impulse type noise while preserving edges in images [1]. Unfortunately, such filters perform poorly when the spatial density of the noise is high [3,6]. As an alternative, gray-scale morphological approaches involving such operations as opening, closing or combinations thereof can be applied to reduce noise in gray-scale images [1–5]. Even in this case, features that are thin or small tend to be filtered out along with the noise [6]. Prior attempts to remedy the problem have relied on the use of multi-resolution (or multi-scale) morphological filters using an array of structuring element sizes. Such algorithms tend to be overly complex and computationally expensive to implement [6].

Book Title

Review of Progress in Quantitative Nondestructive Evaluation

Volume

16A

Chapter

Chapter 3: Signal Processing and Image Analysis

Section

Signal Processing

Pages

725-732

DOI

10.1007/978-1-4615-5947-4_95

Language

en

File Format

application/pdf

Share

COinS
 
Jan 1st, 12:00 AM

A New Morphological Approach for Removing Speckle Noise and Emphasizing Defect Features in Ultrasonic Images

Brunswick, ME

Ultrasonic NDE images are often contaminated with speckle noise. The degradation caused by the presence of speckle noise makes it difficult to identify features of interest that are typically thin or small in nature. A variety of techniques have been proposed to date for reducing such noise. As an example, lowpass filters can be employed to reduce speckle noise. However, they tend to blur thin features and edges. Median filters are also used widely to remove impulse type noise while preserving edges in images [1]. Unfortunately, such filters perform poorly when the spatial density of the noise is high [3,6]. As an alternative, gray-scale morphological approaches involving such operations as opening, closing or combinations thereof can be applied to reduce noise in gray-scale images [1–5]. Even in this case, features that are thin or small tend to be filtered out along with the noise [6]. Prior attempts to remedy the problem have relied on the use of multi-resolution (or multi-scale) morphological filters using an array of structuring element sizes. Such algorithms tend to be overly complex and computationally expensive to implement [6].