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Toward Unaligned Guided Thermal Super-Resolution.
- Source :
- IEEE Transactions on Image Processing; 2022, Vol. 31, p433-445, 13p
- Publication Year :
- 2022
-
Abstract
- Thermography is a useful imaging technique as it works well in poor visibility conditions. High-resolution thermal imaging sensors are usually expensive and this limits the general applicability of such imaging systems. Many thermal cameras are accompanied by a high-resolution visible-range camera, which can be used as a guide to super-resolve the low-resolution thermal images. However, the thermal and visible images form a stereo pair and the difference in their spectral range makes it very challenging to pixel-wise align the two images. The existing guided super-resolution (GSR) methods are based on aligned image pairs and hence are not appropriate for this task. In this paper, we attempt to remove the necessity of pixel-to-pixel alignment for GSR by proposing two models: the first one employs a correlation-based feature-alignment loss to reduce the misalignment in the feature-space itself and the second model includes a misalignment-map estimation block as a part of an end-to-end framework that adequately aligns the input images for performing guided super-resolution. We conduct multiple experiments to compare our methods with existing state-of-the-art single and guided super-resolution techniques and show that our models are better suited for the task of unaligned guided super-resolution from very low-resolution thermal images. [ABSTRACT FROM AUTHOR]
- Subjects :
- IMAGING systems
THERMOGRAPHY
THERMAL imaging cameras
IMAGE sensors
Subjects
Details
- Language :
- English
- ISSN :
- 10577149
- Volume :
- 31
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Image Processing
- Publication Type :
- Academic Journal
- Accession number :
- 170077059
- Full Text :
- https://doi.org/10.1109/TIP.2021.3130538