Overcoming the Domain Gap Between Multi-Band NIR and RGB for Colorization

Near-infrared (NIR) imaging has been widely utilized in low-light applications due to its ability to capture fine textures with less noise compared to RGB sensors, but the absence of color information makes NIR imagery less intuitive for human perception. To bridge this perceptual gap, NIR colorization aims to translate NIR images into visually interpretable RGB representations. In particular, multi-band NIR colorization has attracted growing attention for its potential to leverage complementary spectral information across multiple wavelengths, enabling more accurate NIR-to-RGB mappings. Despite this progress, the inherent domain gap between multi-band NIR and RGB introduces structural ambiguities that hinder accurate reconstruction. To address this issue, we propose a two-stage NIR-to-Visible (N2V) colorization module designed to progressively align NIR representations with the visible domain. In the first stage, a Distance-Assisted Deformable Convolution (DA-DCN) module enhances spatial adaptability to effectively capture fine-grained patterns and structural details within the NIR domain. In the second stage, a Deformable Distillation mechanism transfers spatial sampling priors from a teacher network with auxiliary grayscale input to a student network that relies solely on multi-band NIR data, thereby facilitating domain alignment and suppressing visual artifacts. Extensive experiments demonstrate that the proposed method consistently surpasses state-of-the-art approaches in both quantitative metrics and qualitative perception.