Multimodal sensing is essential in order to reach the robustness required of autonomous vehicle perception systems. Infrared (IR) imaging is of particular interest due to its low cost and complementarity with traditional RGB sensors. However, the lack of IR data in many datasets and simulation tools limits the development and validation of sensor fusion algorithms that exploit this complementarity. To address this, we propose an augmentation method that synthesizes realistic IR data from RGB images using gradient-boosting decision trees. We demonstrate that this method is an effective alternative to traditional deep learning methods for image translation such as CNNs and GANs, particularly in data-scarce situations. The proposed approach generates high-quality synthetic IR, i.e., Near-Infrared (NIR) and thermal images from RGB images, enhancing datasets such as MS2, EPFL, and Freiburg. Our synthetic images exhibit good visual quality when evaluated using metrics such as R2, PSNR, SSIM, and LPIPS, achieving an R2 of 0.98 on the MS2 dataset and a PSNR of 21.3 dB on the Freiburg dataset. We also discuss the application of this method to synthetic RGB images generated by the CARLA simulator for autonomous driving. Our approach provides richer datasets with a particular focus on IR modalities for sensor fusion along with a framework for generating a wider variety of driving scenarios within urban driving datasets, which can help to enhance the robustness of sensor fusion algorithms.