提出一种用于短波红外人脸图像与可见光人脸图像翻译的改进CycleGAN框架。基于CycleGAN框架,新增了损失函数计算通路并设计了新损失函数。建立数据集并通过实验调整模型参数,改进模型在人脸图像上的翻译效果,有效克服光谱特性不同带来的图像模态差异,提升了图像的可观察性。在自建数据集上进行实验验证,将所提框架与其他常用框架从主观评价、FID(Fréchet inception distance)及识别准确率三个方面进行比较。结果表明,所提框架提升效果明显,更好地保持了原目标的结构特征,有效提升了图像翻译结果的可观察性和识别准确率。

We proposed an improved CycleGAN framework for translating short-wavelength infrared facial images and visible-light facial images. Based on the CycleGAN framework, a loss function calculation path was added and a new loss function was designed. A dataset was established, and the model parameters were adjusted based on experiments to improve the translation effect of the proposed model on the facial images. It effectively overcame the differences in images caused by different spectral characteristics so that the images could be easily recognized. The experimental verification was performed with a self-built dataset. The subjective evaluation, FID(Fréchet inception distance), and recognition accuracy were used to compare the proposed framework with several other frameworks. The results show that the improvement of the proposed framework is obvious and the structural features of the original target are better maintained, which effectively improves the observability and recognition accuracy of image translation results.