Image watermarking in the Hermite transform domain with resistance to geometric distortions

2008 ◽  
Author(s):  
Nadia Baaziz ◽  
Boris Escalante-Ramirez ◽  
Oscar Romero-Hernández
Keyword(s):  
Image Watermarking ◽  
Transform Domain ◽  
Hermite Transform ◽  
Geometric Distortions

FHSS signal sparsification in the Hermite transform domain

2016 ◽  
Author(s):  
Milos Brajovic ◽  
Andjela Draganic ◽  
Irena Orovic ◽  
Srdjan Stankovic
Keyword(s):  
Transform Domain ◽  
Hermite Transform

scholarly journals Compressive Sensing in Signal Processing: Algorithms and Transform Domain Formulations

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Irena Orović ◽  
Vladan Papić ◽  
Cornel Ioana ◽  
Xiumei Li ◽  
Srdjan Stanković
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Compressive Sensing ◽  
Signal Reconstruction ◽  
Signal Acquisition ◽  
Transform Domain ◽  
Hermite Transform ◽  
Time Frequency ◽  
Reconstruction Methods ◽  
Fourier Transform Domain

Compressive sensing has emerged as an area that opens new perspectives in signal acquisition and processing. It appears as an alternative to the traditional sampling theory, endeavoring to reduce the required number of samples for successful signal reconstruction. In practice, compressive sensing aims to provide saving in sensing resources, transmission, and storage capacities and to facilitate signal processing in the circumstances when certain data are unavailable. To that end, compressive sensing relies on the mathematical algorithms solving the problem of data reconstruction from a greatly reduced number of measurements by exploring the properties of sparsity and incoherence. Therefore, this concept includes the optimization procedures aiming to provide the sparsest solution in a suitable representation domain. This work, therefore, offers a survey of the compressive sensing idea and prerequisites, together with the commonly used reconstruction methods. Moreover, the compressive sensing problem formulation is considered in signal processing applications assuming some of the commonly used transformation domains, namely, the Fourier transform domain, the polynomial Fourier transform domain, Hermite transform domain, and combined time-frequency domain.

scholarly journals A Robust Image Watermarking Approach Using Cycle Variational Autoencoder

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Qiang Wei ◽  
Hu Wang ◽  
Gongxuan Zhang
Keyword(s):  
Copyright Protection ◽  
Cloud Storage ◽  
Image Watermarking ◽  
Rapid Development ◽  
Transform Domain ◽  
Watermark Image ◽  
Visual Effects ◽  
Variational Autoencoder ◽  
Robust Image ◽  
Traditional Approaches

With the rapid development of Internet and cloud storage, data security sharing and copyright protection are becoming more and more important. In this paper, we introduce a robust image watermarking algorithm for copyright protection based on variational autoencoder networks. The proposed image watermarking embedding and extracting network consists of three parts: encoder subnetwork, decoder subnetwork, and detector subnetwork. In the training process, the encoder and decoder subnetworks learn a robust image representation model and further implement the embedding of 1-bit watermark image to the cover image. Meanwhile, the detector subnetwork learns to extract the 1-bit watermark image from the embedding image. Experimental results demonstrate that the watermarked images generated by the proposed algorithm have better visual effects and are more robust against geometric and noise attacks than traditional approaches in the transform domain.

Representation of uniformly sampled signals in the Hermite transform domain

2016 ◽  
Author(s):  
Milos Brajovic ◽  
Irena Orovic ◽  
Milos Dakovic ◽  
Srdan Stankovic
Keyword(s):  
Transform Domain ◽  
Hermite Transform
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