Thermal infrared imaging has great potential for applications in auto-vehicle, human-machine interactive, surveillance, and defense systems owing to its performance in low-light and low-visible conditions. Nevertheless, the shortage of published datasets and benchmarks in the infrared domain has hampered the development of contemporary research. To tackle this problem, we propose a method to produce synthetic thermal infrared (TIR) images using a diffusion-based image-to-image translation model to enrich the data for learning. The model translates the abundantly available Red, Green, Blue (RGB) images to synthetic TIR data closer to the domain of authentic TIR images. For this purpose, we explore the usage of an unpaired image translation neural model based on Schrödinger bridge algorithms. In addition, the visual characteristic of the object in the image is an important consideration in generating results. Thus, we take advantage of a segmentation module before the image-to-image translation model to discriminate the background and object regions. We practice the model’s performance with a self-proposed dataset comprising unpairs realistic RGB-TIR images. We evaluated the model’s performance in synthesizing thermal images by comparing them to our original thermal dataset, achieving a Fréchet inception distance score of ∼80, indicative of high-quality image generation. In particular, the synthesized images of our model increase the classification accuracy by 15% when only realistic TIR images are used.