Grayscale-Assisted RGB Image Conversion from Near-Infrared Images

Near-InfraRed (NIR) imaging technology plays a pivotal role in assisted driving and safety surveillance systems, yet its monochromatic nature and deficiency in detail limit its further application. Recent methods aim to recover the corresponding RGB image directly from the NIR image using Convolutional Neural Networks (CNN). However, these methods struggle with accurately recovering both luminance and chrominance information and the inherent deficiencies in NIR image details. In this paper, we propose grayscale-assisted RGB image restoration from NIR images to recover luminance and chrominance information in two stages. We address the complex NIR-to-RGB conversion challenge by decoupling it into two separate stages. First, it converts NIR to grayscale images, focusing on luminance learning. Then, it transforms grayscale to RGB images, concentrating on chrominance information. In addition, we incorporate frequency domain learning to shift the image processing from the spatial domain to the frequency domain, facilitating the restoration of the detailed textures often lost in NIR images. Empirical evaluations of our grayscale-assisted framework and existing state-of-the-art methods demonstrate its superior performance and yield more visually appealing results. Code is accessible at: https://github.com/Yiiclass/RING