Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Upsampling

Upsampling

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Risk Factors for Diabetic Foot Ulcers and the Role of Thermal Imaging in Early Detection.

Cureus·2026

Same author

An Evaluation of the Effect of Dimple Insoles on Foot Temperature in Diabetic Patients.

Sensors (Basel, Switzerland)·2025

Same author

Phase quantification using deep neural network processing of XRD patterns.

IUCrJ·2024

Same author

Diabetic foot thermal image segmentation using Double Encoder-ResUnet (DE-ResUnet).

Journal of medical engineering & technology·2022

Same author

Segmentation of Plantar Foot Thermal Images Using Prior Information.

Sensors (Basel, Switzerland)·2022

Same author

Assessment of the non-Gaussianity and non-linearity levels of simulated sEMG signals on stationary segments.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology·2017

Search research articles

Related Experiment Video

Updated: Jul 12, 2026

Simultaneously Capturing Real-time Images in Two Emission Channels Using a Dual Camera Emission Splitting System: Applications to Cell Adhesion

Simultaneously Capturing Real-time Images in Two Emission Channels Using a Dual Camera Emission Splitting System: Applications to Cell Adhesion

Published on: September 4, 2013

Cam-Unet: Print-Cam Image Correction for Zero-Bit Fourier Image Watermarking.

Said Boujerfaoui¹, Hassan Douzi¹, Rachid Harba²

¹IRF-SIC, University of Ibn Zohr, Agadir 80000, Morocco.

Sensors (Basel, Switzerland)

|June 19, 2024

Summary

This study introduces Cam-Unet, a neural network that corrects perspective distortions in smartphone images of watermarked documents. This improves automated detection for secure image watermarking in print-cam applications.

Keywords:

Fourier transform deep learning digital images geometric distortions image watermarking neural networks print-cam watermarking robustness

More Related Videos

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Published on: February 23, 2017

Related Experiment Videos

Last Updated: Jul 12, 2026

Simultaneously Capturing Real-time Images in Two Emission Channels Using a Dual Camera Emission Splitting System: Applications to Cell Adhesion

Simultaneously Capturing Real-time Images in Two Emission Channels Using a Dual Camera Emission Splitting System: Applications to Cell Adhesion

Published on: September 4, 2013

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Published on: February 23, 2017

Area of Science:

Computer Vision
Digital Image Processing
Information Security

Background:

Image watermarking is crucial for authentication, especially in print-cam processes using smartphones.
Perspective distortions in captured images hinder automated information detection.
Existing methods struggle with the challenges of non-structured capture conditions.

Purpose of the Study:

To develop a robust image watermarking system for the print-cam process.
To address and correct perspective distortions in captured watermarked images.
To enhance the reliability of automated information detection from distorted images.

Main Methods:

Proposed Cam-Unet, an end-to-end neural network for image rectification.
Created a large-scale synthetic dataset simulating print-cam attacks.
Combined Fourier transform-based watermarking with Cam-Unet for distortion correction.
Utilized data augmentation techniques to improve network generalization.

Main Results:

Cam-Unet effectively predicts mappings from distorted to rectified images.
The proposed watermarking system outperforms existing methods against print-cam attacks.
Achieved an optimal balance between efficiency and cost-effectiveness.
Demonstrated superior performance in automated information detection post-correction.

Conclusions:

The integrated system offers a significant advancement in secure image watermarking for mobile capture.
Cam-Unet provides a viable solution for perspective distortion in print-cam scenarios.
The method enhances the practical application of image authentication in real-world conditions.