Name
Title | ||
|---|---|---|
A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution |
Abstract | ||
|---|---|---|
This paper presents a transfer domain strategy to tackle the limitations of low-resolution thermal sensors and generate higher-resolution images of reasonable quality. The proposed technique employs a CycleGAN architecture and uses a ResNet as an encoder in the generator along with an attention module and a novel loss function. The network is trained on a multi-resolution thermal image dataset acquired with three different thermal sensors. Results report better performance benchmarking results on the 2nd CVPR-PBVS-2021 thermal image super-resolution challenge than state-of-the-art methods. The code of this work is available online. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/s22062254 | SENSORS |
Keywords | DocType | Volume |
thermal image super-resolution, unsupervised super-resolution, thermal images, attention module, semiregistered thermal images | Journal | 22 |
Issue | ISSN | Citations |
6 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rafael E Rivadeneira | 1 | 0 | 0.34 |
| Angel Domingo Sappa | 2 | 565 | 33.54 |
| Boris X Vintimilla | 3 | 0 | 0.34 |
| Riad Hammoud | 4 | 0 | 0.34 |