A Fusion-Based Framework for Unsupervised Single Image Super-Resolution

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


Image super-resolution has been a continuously demanding topic in the computer-vision community in recent decades and has witnessed impressive applications in increasing spatial resolution in every field like medicine, agriculture, remote sensing, defense security, and many more applications. Further, deep learning-based image super resolution methods have shown tremendous improvement in reconstruction performance. However, most of the recent state-of-the-art deep learning-based methods for image super-resolution assume an ideal degradation by the bicubic kernel on standard dataset approaches and perform poorly on real-world satellite images in practice, as real degradations are far away and more complex in nature than pre-defined assumed kernels. Motivated by this real-time challenge, our idea is to enhance the 600 m spatial-resolution image, which is extremely low, and implicitly defines image-specific features in an iterative way without defining any fixed explicit degradation for image super-resolution. Besides, we also did a comparative study based on a No-Reference Image Quality Assessment. The evaluation is done both qualitatively (vision based) and quantitatively without recurring to a reference image for quality assessment. The proposed framework outperforms by incorporating domain knowledge from recently implemented unsupervised single-image blind super-resolution techniques.

Original languageEnglish
Title of host publicationCyber Security, Cryptology, and Machine Learning - 7th International Symposium, CSCML 2023, Proceedings
EditorsShlomi Dolev, Ehud Gudes, Pascal Paillier
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783031346705
StatePublished - 1 Jan 2023
Event7th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2023 - Be'er Sheva, Israel
Duration: 29 Jun 202330 Jun 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13914 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2023
CityBe'er Sheva


  • Data fusion
  • Feature estimation
  • Super-resolution
  • Unsupervised image super-resolution

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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