In this paper, we address the particularly challenging problem of calibrating a stereo pair of low resolution (80 × 60) thermal cameras. We propose a new calibration method for such setup, based on sub-pixel image analysis of an adequate calibration pattern and bootstrap methods. The experiments show that the method achieves robust calibration with a quarter-pixel re-projection error for an optimal set of 35 input stereo pairs of the calibration pattern, which namely outperforms the standard OpenCV stereo calibration procedure.

In this work, a super-resolution method is proposed for indoor scenes captured by low-resolution thermal cameras. The proposed method is called Edge Focused Thermal Superresolution (EFTS) which contains an edge extraction module enforcing the neural networks to focus on the edge of images. Utilizing edge information, our model, based on residual dense blocks, can perform super-resolution for thermal images, while enhancing the visual information of the edges. Experiments on benchmark datasets showed that our EFTS method achieves better performance in comparison to the state-of-the-art techniques.

No abstract