Summary of Thermal Image Calibration and Correction Using Unpaired Cycle-consistent Adversarial Networks, by Hossein Rajoli et al.
Thermal Image Calibration and Correction using Unpaired Cycle-Consistent Adversarial Networks
by Hossein Rajoli, Pouya Afshin, Fatemeh Afghah
First submitted to arxiv on: 21 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach aims to enhance the quality of current aerial wildfire datasets by introducing a pipeline based on CycleGAN, which can create a comprehensive, standardized large-scale image dataset. This paper presents a novel fusion method that integrates paired RGB images as attribute conditioning in the generators of both directions, improving the accuracy of the generated images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a solution to enhance current aerial wildfire datasets using deep-learning models and camera technology. The proposed approach creates a comprehensive, standardized large-scale image dataset for wildfire detection and characterization. This can help develop more accurate models and improve wildfire monitoring. |
Keywords
* Artificial intelligence * Deep learning
