EMD Algorithm for Robust Image Watermarking

2014 ◽  
Vol 984-985 ◽  
pp. 1255-1260 ◽  
Author(s):  
S. Kannadhasan ◽  
R. Suresh
Keyword(s):  
Performance Evaluation ◽  
Empirical Mode Decomposition ◽  
Image Watermarking ◽  
Image Data ◽  
Digital Information ◽  
Wavelet Domain ◽  
Mode Decomposition ◽  
Index Terms ◽  
Robust Image ◽  
Security Index

Watermarking is process of hiding digital information into another object/signal. To attain robustness information based on image data is transformed in multiple resolution using Wavelet based image watermarking methods. Embedding watermark bits in middle frequency sub images in the wavelet domain is done using Multiband Wavelet Transform (MWT). At this juncture an attempt is made to analyze robustness for different test images. To resist various attacks Empirical Mode Decomposition (EMD) is used. Performance evaluation of an Image watermarking includes robustness, imperceptibility, watermark capacity and security. Index Terms Image enhancement Empirical mode decomposition, Multiband wavelets transformation

Robust Watermarking Algorithm Based on Bi-Dimensional Empirical Mode Decomposition

2011 ◽  
Vol 204-210 ◽  
pp. 627-631
Author(s):  
Ming Hui Deng ◽  
Fang Yang ◽  
Run Tao Wang
Keyword(s):  
Empirical Mode Decomposition ◽  
Image Watermarking ◽  
Image Theory ◽  
Geometric Distortion ◽  
Robust Watermarking ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Robust Image ◽  
Characteristics Of Image ◽  
Frequency Properties

In this paper, we introduce a robust image watermarking method based on bi-dimensional empirical mode decomposition against geometric distortion. Based on the characteristics of the image theory and human visual system, the proposed method makes use of orthogonal properties of empirical mode decomposition to achieve the bi-dimensional empirical mode decomposition transform on the image. The image is decomposed into a series of IMFs and residue which contain the different frequency parts of the image. So the watermark is adaptively weighed to the different positions of the middle frequency IMF region. The method makes use of the multi-scale analysis characteristics of image bi-dimensional empirical mode decomposition theory and the person’s sense of vision and shows excellent advantage against shearing attack. The method could show the watermark clearly when half of the image has been cut. Experimental results show this method excellent robustness for image shearing. The watermark thus generated is invisible and performs well in StirMark test and is robust to geometrical attacks. Compared with other watermarking algorithms, this algorithm is more robust, especially against geometric distortion, while having excellent frequency properties.

Robust Image Watermarking Based on Multiband Wavelets and Empirical Mode Decomposition

2007 ◽  
Vol 16 (8) ◽  
pp. 1956-1966 ◽  
Author(s):  
Ning Bi ◽  
Qiyu Sun ◽  
Daren Huang ◽  
Zhihua Yang ◽  
Jiwu Huang
Keyword(s):  
Empirical Mode Decomposition ◽  
Image Watermarking ◽  
Mode Decomposition ◽  
Robust Image

Image Fusion in Hyperspectral Image Classification using Genetic Algorithm

2016 ◽  
Vol 2 (3) ◽  
pp. 703 ◽  
Author(s):  
B. Saichandana ◽  
K. Srinivas ◽  
R. KiranKumar
Keyword(s):  
Genetic Algorithm ◽  
Image Fusion ◽  
Image Classification ◽  
Empirical Mode Decomposition ◽  
Hyperspectral Image ◽  
Image Data ◽  
Hyperspectral Data ◽  
Hyperspectral Image Classification ◽  
Mode Decomposition ◽  
Fused Image

<p>Hyperspectral remote sensors collect image data for a large number of narrow, adjacent spectral bands. Every pixel in hyperspectral image involves a continuous spectrum that is used to classify the objects with great detail and precision. This paper presents hyperspectral image classification mechanism using genetic algorithm with empirical mode decomposition and image fusion used in preprocessing stage. 2-D Empirical mode decomposition method is used to remove any noisy components in each band of the hyperspectral data. After filtering, image fusion is performed on the hyperspectral bands to selectively merge the maximum possible features from the source images to form a single image. This fused image is classified using genetic algorithm. Different indices, such as K-means (KMI), Davies-Bouldin Index (DBI), and Xie-Beni Index (XBI) are used as objective functions. This method increases classification accuracy of hyperspectral image.</p>

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