TY - GEN
T1 - A Fusion-Based Framework for Unsupervised Single Image Super-Resolution
AU - Mishra, Divya
AU - Dror, Itai
AU - Hadar, Ofer
AU - Choukroun, Daniel
AU - Maman, Shimrit
AU - Blumberg, Dan G.
N1 - Funding Information:
This work is supported by a grant (Grant No. 3-17380) from the Ministry of Science and Technology, Israel.
Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - 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.
AB - 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.
KW - Data fusion
KW - Feature estimation
KW - Super-resolution
KW - Unsupervised image super-resolution
UR - http://www.scopus.com/inward/record.url?scp=85164964683&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-34671-2_7
DO - 10.1007/978-3-031-34671-2_7
M3 - Conference contribution
AN - SCOPUS:85164964683
SN - 9783031346705
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 85
EP - 95
BT - Cyber Security, Cryptology, and Machine Learning - 7th International Symposium, CSCML 2023, Proceedings
A2 - Dolev, Shlomi
A2 - Gudes, Ehud
A2 - Paillier, Pascal
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2023
Y2 - 29 June 2023 through 30 June 2023
ER -
