Near-infrared (NIR) image spectrum translation is a challenging problem with many promising applications. Existing methods struggle with the mapping ambiguity between the NIR and the RGB domains, and generalize poorly due to the limitations of models' learning capabilities and the unavailability of sufficient NIR-RGB image pairs for training. To address these challenges, we propose a cooperative learning paradigm that colorizes NIR images in parallel with another proxy grayscale colorization task by exploring latent cross-domain priors (i.e., latent spectrum context priors and task domain priors), dubbed CoColor. The complementary statistical and semantic spectrum information from these two task domains -- in the forms of pre-trained colorization networks -- are brought in as task domain priors. A bilateral domain translation module is subsequently designed, in which intermittent NIR images are generated from grayscale and colorized in parallel with authentic NIR images; and vice versa for the grayscale images. These intermittent transformations act as latent spectrum context priors for efficient domain knowledge exchange. We progressively fine-tune and fuse these modules with a series of pixel-level and feature-level consistency constraints. Experiments show that our proposed cooperative learning framework produces satisfactory spectrum translation outputs with diverse colors and rich textures, and outperforms state-of-the-art counterparts by 3.95dB and 4.66dB in terms of PNSR for the NIR and grayscale colorization tasks, respectively.

翻译：近红外（NIR）图像光谱翻译是一个具有挑战性的问题，拥有众多前景广阔的应用。现有方法难以解决近红外与RGB域之间的映射歧义，且由于模型学习能力的局限以及训练中可用的近红外-红外图像对不足，导致泛化能力较差。为应对这些挑战，我们提出一种合作学习范式，通过探索潜在跨领域先验（即潜在光谱上下文先验和任务领域先验），将NIR图像着色与另一个代理灰度着色任务并行执行，并将其命名为CoColor。来自这两个任务领域的互补性统计与语义光谱信息——以预训练着色网络的形式——被引入作为任务领域先验。随后，我们设计了双边域翻译模块，其中间歇性NIR图像由灰度图生成并与真实NIR图像并行着色；反之亦然。这些间歇性变换作为潜在光谱上下文先验，用于高效的知识域交换。我们通过一系列像素级与特征级一致性约束，逐步微调并融合这些模块。实验表明，我们提出的合作学习框架能产生具有多样色彩和丰富纹理且令人满意的光谱翻译输出，在NIR与灰度着色任务中，相较于最先进方法，PNSR分别提升了3.95dB和4.66dB。