Transformation of thermal infrared (TIR) images into visual, i.e. perceptually realistic color (RGB) images, is a challenging problem. TIR cameras have the ability to see in scenarios where vision is severely impaired, for example in total darkness or fog, and they are commonly used, e.g., for surveillance and automotive applications. However, interpretation of TIR images is difficult, especially for untrained operators. Enhancing the TIR image display by transforming it into a plausible, visual, perceptually realistic RGB image presumably facilitates interpretation. Existing grayscale to RGB, so called, colorization methods cannot be applied to TIR images directly since those methods only estimate the chrominance and not the luminance. In the absence of conventional colorization methods, we propose two fully automatic TIR to visual color image transformation methods, a two-step and an integrated approach, based on Convolutional Neural Networks. The methods require neither pre-nor postprocessing, do not require any user input, and are robust to image pair misalignments. We show that the methods do indeed produce perceptually realistic results on publicly available data, which is assessed both qualitatively and quantitatively.

https://www.diva-portal.org/smash/get/diva2:1229000/FULLTEXT02.pdf

热成像技术具有低光照、浓雾条件下正常的工作的优点，现在广泛应用于监控，无人驾驶等领域。但是，对于IR影像的判读工作是不容易的，尤其是对于那些未经训练的工作者。所以，对IR影像进行彩色化的增强，使其具有可读性是有意义的。传统的灰度图像上色模型无法直接应用于IR图像，因为他们评估的是色度而非亮度。本文提出了一种全自动化的IR图像上色方法，模型可以分为一个2分步模型和一个合并模型，模型不需要额外的前处理后处理工作。实验证明模型可以生成符合直觉的逼真的彩色化IR图像。