Year 2025

• Generative Diffusion Models for Compressed Sensing of Satellite LiDAR Data: Evaluating Image Quality Metrics in Forest Landscape Reconstruction

  Publication

  • A. Ramirez-Jaime
  • G. Arce
  • N. Porraz-Diaz
  • O. Ieremeiev
  • A. Rubel
  • V. Lukin
  • M. Kopytek
  • P. Lech
  • J. Fastowicz
  • K. P. Okarma

  - Remote Sensing - Year 2025

  Spaceborne LiDAR systems are crucial for Earth observation but face hardware constraints, thus limiting resolution and data processing. We propose integrating compressed sensing and diffusion generative models to reconstruct high-resolution satellite LiDAR data within the Hyperheight Data Cube (HHDC) framework. Using a randomized illumination pattern in the imaging model, we achieve efficient sampling and compression, reducing...

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Year 2023

• МЕТОДИ ІНТЕЛЕКТУАЛЬНОЇ ОБРОБКИ ЗОБРАЖЕНЬ ТА ВІДЕО НА ОСНОВІ МЕТРИК ВІЗУАЛЬНОЇ ЯКОСТІ ДЛЯ ПЕРСПЕКТИВНИХ ЗАСТОСУВАНЬ

  Publication

  • S. Abramov
  • J. Fastowicz
  • V. Lukin
  • V. Abramova
  • O. Rubel
  • K. P. Okarma
  • P. Lech
  • O. Ieremeiev

  - European Science - Year 2023

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Year 2022

• Combined No-Reference Image Quality Metrics for Visual Quality Assessment Optimized for Remote Sensing Images

  Publication

  • A. Rubel
  • O. Ieremeiev
  • V. Lukin
  • J. Fastowicz
  • K. P. Okarma

  - Applied Sciences-Basel - Year 2022

  No-reference image quality assessment is one of the most demanding areas of image analysis for many applications where the results of the analysis should be strongly correlated with the quality of an input image and the corresponding reference image is unavailable. One of the examples might be remote sensing since the transmission of such obtained images often requires the use of lossy compression and they are often distorted,...

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