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Sivakumar, D.
- A Novel Digital Image Watermarking Scheme Using Biorthogonal Wavelets
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Authors
G. Yamuna
1,
D. Sivakumar
2
Affiliations
1 Department of Electrical and Electronics Engineering, Annamalai University, Tamil Nadu, IN
2 Department of Electronics and Instrumentation Engineering, Annamalai University, Tamil Nadu, IN
1 Department of Electrical and Electronics Engineering, Annamalai University, Tamil Nadu, IN
2 Department of Electronics and Instrumentation Engineering, Annamalai University, Tamil Nadu, IN
Source
ICTACT Journal on Image and Video Processing, Vol 1, No 4 (2011), Pagination: 236-240Abstract
Copyright protection is considered as an issue of vital significance owing to the escalating utilization of internet and effortless copying, tampering and distribution of digital images. Digital watermarking methodologies are looked upon as a competent tool for safeguarding the digital images from copyright infringements issues. A number of researches in existence deal with copyright protection with the aid of watermarking. Recently, wavelet domain based watermarking ap-proaches are gaining popularity in watermarking researches. In this paper we have proposed a novel watermarking scheme for copyright protection in digital images. The watermarking is performed in wave-let domain using bi-orthogonal wavelet transform. As the proposed approach is non-blind, it requires original image for extracting the watermark. The watermark image is a binary image. The watermark image is embedded in the HH sub-band of the wavelet transformed original image. A Good quality of watermarked image is assured through the proposed scheme from the higher PSNR values which is evident from experimental result.Keywords
Digital Watermarking, Copyright Protection, Non-Blind, Discrete Wavelet Transform, Bi-Orthogonal.
- Dimensionality Reduction based Classification Using Generative Adversarial Networks Dataset Generation
Abstract Views :73 |
PDF Views:1
Authors
G. Narendra
1,
D. Sivakumar
1
Affiliations
1 Department of Electronics and Instrumentation Engineering, Annamalai University, IN
1 Department of Electronics and Instrumentation Engineering, Annamalai University, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 1 (2022), Pagination: 2786-2790Abstract
The term data augmentation refers to an approach that can be used to prevent overfitting in the training dataset, which is where the issue first manifests itself. This is based on the assumption that extra datasets can be improved by include new information that is of use. It is feasible to create an artificially larger training dataset by utilizing methods such as data warping and oversampling. This will allow for the creation of more accurate models. This idea is demonstrated through the application of a variety of different methods, some of which include neural style transfer, adversarial training, and erasure by random erasure, amongst others. By utilizing oversampling augmentations, it is feasible to create synthetic instances that can be incorporated into the training data. This is made possible by the generation of synthetic instances. There are numerous illustrations of this, including image merging, feature space enhancements, and generative adversarial networks, to name a few (GANs). In this paper, we aim to provide evidence that a Generative Adversarial Network can be used to convert regular images into Hyper Spectral Images (HSI). The purpose of the model is to generate data by including a certain amount of unpredictable noise.Keywords
Data Augmentation, GAN, Hyper Spectral Images, Classification.References
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