RGB-NIR fusion is a promising method for low-light imaging. However, high-intensity noise in low-light images amplifies the effect of structure inconsistency between RGB-NIR images, which fails existing algorithms. To handle this, we propose a new RGB-NIR fusion algorithm called Dark Vision Net (DVN) with two technical novelties: Deep Structure and Deep Inconsistency Prior (DIP). The Deep Structure extracts clear structure details in deep multiscale feature space rather than raw input space, which is more robust to noisy inputs. Based on the deep structures from both RGB and NIR domains, we introduce the DIP to leverage the structure inconsistency to guide the fusion of RGB-NIR. Benefiting from this, the proposed DVN obtains high-quality lowlight images without the visual artifacts. We also propose a new dataset called Dark Vision Dataset (DVD), consisting of aligned RGB-NIR image pairs, as the first public RGBNIR fusion benchmark. Quantitative and qualitative results on the proposed benchmark show that DVN significantly outperforms other comparison algorithms in PSNR and SSIM, especially in extremely low light conditions.

翻译：RGB-NIR融合是实现低光照成像的一种有效方法。然而，低光照图像中的高强度噪声会放大RGB-NIR图像间结构不一致性的影响，导致现有算法失效。针对这一问题，我们提出了一种名为DarkVisionNet（DVN）的RGB-NIR融合算法，其中包含两项技术创新：深层结构与深度不一致性先验（DIP）。深层结构在深层次多尺度特征空间而非原始输入空间中提取清晰的结构细节，因此对噪声输入具有更强的鲁棒性。基于RGB和NIR两个域的深层结构，我们引入DIP利用结构不一致性来指导RGB-NIR融合。得益于此，所提出的DVN能够获得高质量的低光照图像，且无视觉伪影。我们还构建了一个名为DarkVisionDataset（DVD）的新数据集，该数据集由对齐的RGB-NIR图像对组成，作为首个公开的RGB-NIR融合基准。在提出的基准上进行的定量与定性结果表明，DVN在PSNR和SSIM指标上显著优于其他对比算法，尤其是在极低光照条件下。