TIC: text-guided image colorization using conditional generative model

Image colorization is a well-known problem in computer vision. However, due to the ill-posed nature of the task, image colorization is inherently challenging. Though several attempts have been made by researchers to make the colorization pipeline automatic, these processes often produce unrealistic results due to a lack of conditioning. In this work, we attempt to integrate textual descriptions as an auxiliary condition, along with the grayscale image that is to be colorized, to improve the fidelity of the colorization process. To the best of our knowledge, this is one of the first attempts to incorporate textual conditioning in the colorization pipeline. To do so, a novel deep network has been proposed that takes two inputs (the grayscale image and the respective encoded text description) and tries to predict the relevant color gamut. As the respective textual descriptions contain color information of the objects present in the scene, the text encoding helps to improve the overall quality of the predicted colors. The proposed model has been evaluated using different metrics like SSIM, PSNR, LPISPS and achieved scores of 0.917, 23.27,0.223, respectively. These quantitative metrics have shown that the proposed method outperforms the SOTA techniques in most of the cases.