Edge-Focus Thermal Image Super-Resolution using Generative Adversarial Network
Nguyễn Đức Thuận, Trinh Phuong Dong, Bui Quang Manh, Hoang Anh Thai, Tran Quang Trung, Hoang Si Hong · 2022
Thermal imaging has played an important role in a wide range of areas of life. However, thermal cameras often produce low-resolution images, which limits the ability to observe objects in thermal imaging applications. Modern thermal cameras often include a built-in high-resolution visible camera to supplement the thermal image information. This paper proposes a method to increase the resolution of thermal images using edge features of corresponding high-resolution visible images. The Canny edge detection and thin-line downscaling algorithms are used to generate edge maps from high-resolution visible images to contribute to the super-resolution network. The proposed super-resolution model is designed based on generative adversarial network architecture for $\times$ 2, $\times$ 3, and $\times$ 4 magnification. The KAIST dataset is used to train and test the model. Peak signal-to-noise ratio (PSNR) and structure similarity index (SSIM) are used to evaluate the quality of super-resolution images. After the training process, to prove the effectiveness of edge features, we compare the quality of the super-resolution images generated from the proposed method with other methods. The comparison results show that the proposed method has the highest performance in terms of PSNR and SSIM.