Cross-Fusion of Band-Specific Spectral Features For Multi-Band NIR Colorization

Near-infrared (NIR) colorization holds the potential to enrich human interpretation and seamlessly integrating with existing visual frameworks, especially in low-light environments. Multi-band NIR colorization has been explored to efficiently uncover correlations between individual NIR bands and RGB, leveraging abundant spectral information than single-band NIR. However, the previous methods simply concatenated the multi-band NIR images, and in this paper, we study a deep learning network to effectively exploit band-specific spectral features for colorization. We propose a network with private and cross-fusion architectures that extracts and reconstructs the private and cross features separately. Private and cross-fusion modules are utilized to preserve the band-specific characteristics of the private features while updating any cross features lost during private convolutions. Experimental results show that the proposed network excels at producing the overall colors of objects and high-frequency details.