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Related Concept Videos

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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Bandpass Sampling01:17

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In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Aliasing01:18

Aliasing

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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
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Upsampling

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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The spatial frequency domain designated watermarking framework uses linear blind source separation for intelligent

Rani Kumari1, Abhijit Mustafi1

  • 1Department of Computer Science, Birla Institute of Technology, Ranchi, India.

Frontiers in Neurorobotics
|November 28, 2022
PubMed
Summary

This study introduces a robust digital watermarking algorithm using fractional Fourier transform for enhanced image security. The method demonstrates strong resilience against common attacks like JPEG compression and noise.

Keywords:
blind source separationdigital watermarkingfractional Fourier transformgenetic algorithmrobustness

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Area of Science:

  • Digital Image Processing
  • Information Security
  • Signal Processing

Background:

  • Digital watermarking is crucial for protecting intellectual property and ensuring data integrity in digital media.
  • Existing methods often face challenges with robustness against various signal processing attacks.
  • The need for secure and imperceptible embedding techniques remains a key research area.

Purpose of the Study:

  • To develop an advanced digital watermarking algorithm with improved robustness and security.
  • To leverage the space-frequency domain for watermark embedding using the fractional Fourier transform.
  • To enhance watermark retrieval accuracy and system strength through heuristic and genetic algorithms.

Main Methods:

  • Embedding watermarks in the space-frequency domain via the fractional Fourier transform.
  • Utilizing blind source separation techniques for watermark extraction.
  • Employing a heuristic algorithm to bolster watermark embedding strength.
  • Optimizing the fractional domain using a genetic algorithm to minimize Root Mean Square Error (RMSE).

Main Results:

  • The developed algorithm demonstrates significant robustness against common attacks, including JPEG compression and Gaussian noise.
  • The heuristic enhancement and genetic algorithm optimization contribute to a stronger watermarking system.
  • The fractional Fourier transform effectively embeds watermarks in the space-frequency domain.
  • Accurate watermark extraction is achieved using blind source separation.

Conclusions:

  • The proposed digital watermarking algorithm offers a robust and effective solution for image security.
  • The integration of fractional Fourier transform and optimization techniques enhances watermark resilience.
  • This approach provides a promising direction for secure digital media applications.