Depth-Conditioned Thermal-like Image Generation
Patricia L. Suárez, Angel Domingo Sappa · 2024
This paper proposes a novel approach to generate thermal-like representations from RGB images by using the corresponding depth map as an additional constraint. The given RGB images are converted to the HSV color space and the brightness channel is used as input together with the spatial information provided by the depth map of the given scene. This depth map is used as prior information by the generative network. By training a generative model with paired input images and their corresponding depth maps, the model learns the mapping from the RGB images to thermal-like representations. Experimental results demonstrate that the method outperforms state-of-the-art approaches, producing superior-quality thermal images with improved shape and sharpness, attributed to using depth maps as complement information.