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Related Concept Videos

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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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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Convolution Properties II01:17

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The important convolution properties include width, area, differentiation, and integration properties.
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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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Convolution Properties I

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Convolution computations can be simplified by utilizing their inherent properties.
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Related Experiment Video

Updated: Dec 7, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

872

Convolutional Neural Network Architecture for Recovering Watermark Synchronization.

Wook-Hyung Kim1, Jihyeon Kang2, Seung-Min Mun3

  • 1Visual Display Division, Samsung Electronics, Suwon 16677, Korea.

Sensors (Basel, Switzerland)
|September 25, 2020
PubMed
Summary

This study introduces a novel convolutional neural network template to resist geometric distortion in digital image watermarking. This method enables robust watermark extraction and recovery of original image geometry.

Keywords:
copyright protectiondeep neural networkdepth-image-based renderingdigital watermarktemplate watermark

Related Experiment Videos

Last Updated: Dec 7, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

872

Area of Science:

  • Computer Science
  • Digital Image Processing
  • Machine Learning

Background:

  • Existing digital watermarking techniques are often vulnerable to geometric distortions like rotation, scaling, and translation.
  • These distortions can render embedded watermarks undetectable or unrecoverable.
  • Robust watermarking is crucial for copyright protection and data integrity.

Purpose of the Study:

  • To propose a novel convolutional neural network (CNN)-based template architecture for robust digital image watermarking.
  • To address the vulnerability of current watermarking methods to geometric distortions.
  • To enable reliable watermark detection and image recovery after geometric transformations.

Main Methods:

  • A CNN-based template architecture comprising three networks: template generation, template extraction, and template matching.
  • Template generation network creates a noise-based template inserted at specific image locations.
  • Template extraction and matching networks detect template locations and estimate geometric distortion parameters.

Main Results:

  • The proposed template architecture effectively compensates for geometric distortions.
  • Accurate estimation of geometric distortion parameters is achieved by comparing template locations.
  • Restoration of the original image geometry is possible using the estimated parameters.

Conclusions:

  • The developed CNN-based template watermarking method offers enhanced robustness against geometric distortions.
  • This approach allows for normal decoding of watermarks even after significant image transformations.
  • The technique provides a viable solution for secure and reliable digital image watermarking.