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April 24, 2026International Journal of Innovative Research in Technology0 citationsOpen Access

A Deep Learning Framework for Semantic Colorization of Synthetic Aperture Radar Images

SSShivaprasad SatlaKakatiya University

Key Points

  • The aim is to improve the visualization of synthetic aperture radar images using a deep learning framework.
  • Developed a deep learning framework for image colorization
  • Tested effectiveness on synthetic aperture radar images
  • Utilized various neural network architectures for optimal results
  • Achieved significant improvements in image clarity and visual appeal
  • Demonstrated enhanced semantic understanding of radar images
  • Achieved high accuracy in matching original image colors

Abstract

Explore the article titled A Deep Learning Framework for Semantic Colorization of Synthetic Aperture Radar Images from IJIRT Volume 12, Issue 9. This study evaluates the effectiveness of teaching programs on waste management knowledge among women.

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Cite This Study

Shivaprasad Satla (2026) studied this question.

synapsesocial.com/papers/69eb0cb2553a5433e34b5a4ahttps://doi.org/10.64643/ijirtv12i9-192068-459
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Using colorization to bridge the synthetic-measured gap2024
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  3. 3Synthetic Aperture Radar Image Classification Using Deep Learning2024 · 3 citations
  4. 4From shades to vibrance: a comprehensive review of modern image colorization techniques2025 · 2 citations
  5. 5Context-Driven Ship, Vehicle, and Aircraft Detection in Colored Synthetic Aperture Radar (SAR) Images2026