A confidence-based approach to enhancing underwater acoustic image formation

1999 ◽  
Vol 8 (2) ◽  
pp. 270-285 ◽  
Author(s):  
V. Murino ◽  
A. Trucco
Keyword(s):  
Image Formation ◽  
Acoustic Image ◽  
Underwater Acoustic

scholarly journals A Performance Analysis of Feature Extraction Algorithms for Acoustic Image-Based Underwater Navigation

2021 ◽  
Vol 9 (4) ◽  
pp. 361
Author(s):  
António José Oliveira ◽  
Bruno Miguel Ferreira ◽  
Nuno Alexandre Cruz
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Feature Detection ◽  
Acoustic Imaging ◽  
Acoustic Image ◽  
Feature Description ◽  
Underwater Acoustic ◽  
Environmental Features ◽  
Acoustic Images ◽  
Underwater Navigation

In underwater navigation, sonars are useful sensing devices for operation in confined or structured environments, enabling the detection and identification of underwater environmental features through the acquisition of acoustic images. Nonetheless, in these environments, several problems affect their performance, such as background noise and multiple secondary echoes. In recent years, research has been conducted regarding the application of feature extraction algorithms to underwater acoustic images, with the purpose of achieving a robust solution for the detection and matching of environmental features. However, since these algorithms were originally developed for optical image analysis, conclusions in the literature diverge regarding their suitability to acoustic imaging. This article presents a detailed comparison between the SURF (Speeded-Up Robust Features), ORB (Oriented FAST and Rotated BRIEF), BRISK (Binary Robust Invariant Scalable Keypoints), and SURF-Harris algorithms, based on the performance of their feature detection and description procedures, when applied to acoustic data collected by an autonomous underwater vehicle. Several characteristics of the studied algorithms were taken into account, such as feature point distribution, feature detection accuracy, and feature description robustness. A possible adaptation of feature extraction procedures to acoustic imaging is further explored through the implementation of a feature selection module. The performed comparison has also provided evidence that further development of the current feature description methodologies might be required for underwater acoustic image analysis.

scholarly journals Underwater Acoustic Image Denoising Using Stationary Wavelet Transform and Various Shrinkage Functions

2021 ◽  
Vol 20 (2) ◽  
Author(s):  
Priyadharsini Ravisankar
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
High Frequency ◽  
Low Frequency ◽  
Stationary Wavelet Transform ◽  
Acoustic Image ◽  
Underwater Acoustic ◽  
Acoustic Images ◽  
Frequency Components ◽  
Shrinkage Functions

Underwater acoustic images are captured by sonar technology which uses sound as a source. The noise in the acoustic images may occur only during acquisition. These noises may be multiplicative in nature and cause serious effects on the images affecting their visual quality. Generally image denoising techniques that remove the noise from the images can use linear and non-linear filters. In this paper, wavelet based denoising method is used to reduce the noise from the images. The image is decomposed using Stationary Wavelet Transform (SWT) into low and high frequency components. The various shrinkage functions such as Visushrink and Sureshrink are used for selecting the threshold to remove the undesirable signals in the low frequency component. The high frequency components such as edges and corners are retained. Then the inverse SWT is used for reconstruction of denoised image by combining the modified low frequency components with the high frequency components. The performance measure Peak Signal to Noise Ratio (PSNR) is obtained for various wavelets such as Haar, Daubechies,Coiflet and by changing the thresholding methods.

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