This paper proposes a novel approach for obtaining high-quality thermal image-like representations that can be used as inputs in various thermal image compressive sensing applications. We address the challenge of low-resolution/quality thermal images by generating synthetic thermal image representations using a contrastive cycled GAN network from low-cost visible images. These representations can then be used to improve the quality of low-quality thermal images of the same scene. Experimental results demonstrate the effectiveness of the proposed approach on different datasets.