Underwater acoustic image quality evaluating system based on semi-fragile watermark

2014 ◽  
Author(s):  
Weiling Chen ◽  
Fei Yuan ◽  
En Cheng
Keyword(s):  
Image Quality ◽  
Acoustic Image ◽  
Underwater Acoustic ◽  
Fragile Watermark

scholarly journals Underwater Acoustic Image Encoding Based on Interest Region and Correlation Coefficient

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Liu Lixin ◽  
Guo Feng ◽  
Wu Jinqiu
Keyword(s):  
Image Quality ◽  
Correlation Coefficient ◽  
Compression Ratio ◽  
Search Range ◽  
Fractal Coding ◽  
Acoustic Image ◽  
Image Encoding ◽  
Underwater Acoustic ◽  
Large Size ◽  
Matching Strategy

It is difficult for the conventional image compression method to achieve good compression effect in the underwater acoustic image (UWAI), because the UWAI has large amount of noise and low correlation between pixel points. In this paper, fractal coding is introduced into UWAI compression, and a fractal coding algorithm based on interest region is proposed according to the importance of different regions in the image. The application problems of traditional quadtree segmentation in UWAIs was solved by the range block segmentation method in the coding process which segmented the interest region into small size and the noninterest region into large size and balanced the compression ratio and the decoded image quality. This paper applies the classification, reduction codebook, and correlation coefficient matching strategy to narrow the search range of the range block in order to solve the problem of the long encoding time and the calculation amount of encoding process is greatly reduced. The experimental results show that the proposed algorithm improves the compression ratio and encoding speed while ensuring the image quality of important regions in the UWAI.

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.

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