Image mosaicking is widely used for privacy protection but severely degrades visual information in affected regions. This research addresses the challenge of restoring these mosaicked areas in visual-spectrum images. We propose a novel approach that leverages co-registered infrared (IR) imagery, which often remains unaffected by such obfuscation, as a guiding modality. Our method employs a Generative Adversarial Network (GAN) architecture specifically designed to learn the mapping between IR features and corresponding visual content. The GAN is trained to inpaint the mosaicked visual patches by generating realistic details conditioned on the IR image data. Experimental evaluations show the proposed method effectively reconstructs plausible and visually coherent content within the mosaicked regions, significantly enhancing image interpretability. This work demonstrates the potential of cross-modal GANs for robust image restoration in challenging scenarios.