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Scaling01:26

Scaling

287
In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
287

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Historical Text Image Enhancement Using Image Scaling and Generative Adversarial Networks.

Sajid Ullah Khan1, Imdad Ullah2, Faheem Khan3

  • 1Multimedia Information Processing Lab, Department of Information and Communication Engineering, Chosun University, Gwangju 61452, Republic of Korea.

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|April 28, 2023
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Summary
This summary is machine-generated.

This study introduces a novel method using wavelet transforms and generative adversarial networks (GANs) to enhance degraded historical text images. The approach effectively improves image resolution, de-noises, and de-blurs documents for better readability and analysis.

Keywords:
generative adversarial networkmachine learningtext image enhancementwavelet transform

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Area of Science:

  • Digital Image Processing
  • Document Analysis
  • Artificial Intelligence

Background:

  • Historical documents often suffer from degraded text quality due to aging, watermarks, and stains.
  • Poor text quality hinders document recognition, analysis, and preservation efforts.
  • Effective text image enhancement is crucial for accessing and utilizing historical records.

Purpose of the Study:

  • To develop an advanced method for enhancing degraded historical text images.
  • To improve image resolution, de-noise, and de-blur historical documents.
  • To enhance spectral and spatial features for clearer text representation.

Main Methods:

  • A hybrid approach combining bi-cubic interpolation of Lifting Wavelet Transform (LWT) and Stationary Wavelet Transform (SWT) for image resolution enhancement.
  • Utilizing a Generative Adversarial Network (GAN) to extract and fuse spectral and spatial features.
  • A two-part process involving wavelet transformation for de-noising/de-blurring and GAN for feature fusion.

Main Results:

  • The proposed method significantly enhances image resolution, effectively de-noising and de-blurring historical text images.
  • The GAN component successfully fuses original and processed images, improving spectral and spatial features.
  • Experimental results demonstrate superior performance compared to existing deep learning methods.

Conclusions:

  • The integrated wavelet transform and GAN approach offers a robust solution for historical text image enhancement.
  • This technique improves the legibility and analytical potential of degraded historical documents.
  • The proposed model represents a significant advancement in digital document restoration and analysis.