TR2019-010
Unrolled Projected Gradient Descent for Multi-Spectral Image Fusion
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- "Unrolled Projected Gradient Descent for Multi-Spectral Image Fusion", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP.2019.8683124, May 2019.BibTeX TR2019-010 PDF
- @inproceedings{Lohit2019may,
- author = {Lohit, Suhas and Liu, Dehong and Mansour, Hassan and Boufounos, Petros T.},
- title = {Unrolled Projected Gradient Descent for Multi-Spectral Image Fusion},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2019,
- month = may,
- doi = {10.1109/ICASSP.2019.8683124},
- url = {https://www.merl.com/publications/TR2019-010}
- }
,
- "Unrolled Projected Gradient Descent for Multi-Spectral Image Fusion", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP.2019.8683124, May 2019.
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MERL Contacts:
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Research Areas:
Abstract:
In this paper, we consider the problem of fusing low spatial resolution multi-spectral (MS) aerial images with their associated high spatial resolution panchromatic image. To solve this problem, various methods have been proposed, using either model-based or modelagnostic algorithms such as deep learning techniques. In this paper, we aim to utilize more interpretable architectures to solve the MS fusion problem by integrating existing ideas from image processing with deep learning. In particular, we develop a signal processinginspired learning solution, where we unroll the iterations of the projected gradient descent (PGD) algorithm, and each iteration contains a projection operation carried out by a deep convolutional neural network. We observe that our proposed method provides a new perspective on existing deep-learning solutions, and under certain circumstance it reduces to current black-box deep learning methods. Our extensive experimental results show significant improvements of the proposed approach over several baselines.
Related News & Events
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NEWS MERL presenting 16 papers at ICASSP 2019 Date: May 12, 2019 - May 17, 2019
Where: Brighton, UK
MERL Contacts: Petros T. Boufounos; Anoop Cherian; Chiori Hori; Toshiaki Koike-Akino; Jonathan Le Roux; Dehong Liu; Hassan Mansour; Tim K. Marks; Philip V. Orlik; Anthony Vetro; Pu (Perry) Wang; Gordon Wichern
Research Areas: Computational Sensing, Computer Vision, Machine Learning, Signal Processing, Speech & AudioBrief- MERL researchers will be presenting 16 papers at the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which is being held in Brighton, UK from May 12-17, 2019. Topics to be presented include recent advances in speech recognition, audio processing, scene understanding, computational sensing, and parameter estimation. MERL is also a sponsor of the conference and will be participating in the student career luncheon; please join us at the lunch to learn about our internship program and career opportunities.
ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event attracts more than 2000 participants each year.
- MERL researchers will be presenting 16 papers at the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which is being held in Brighton, UK from May 12-17, 2019. Topics to be presented include recent advances in speech recognition, audio processing, scene understanding, computational sensing, and parameter estimation. MERL is also a sponsor of the conference and will be participating in the student career luncheon; please join us at the lunch to learn about our internship program and career opportunities.