Blind deblurring of a single infrared image is a challenging computer vision problem. Because the blur is not only caused by the motion of different objects but also by the relative motion and jitter of cameras, there is a change of scene depth. In this work, a method based on the GAN and channel prior discrimination is proposed for infrared image deblurring. Different from the previous work, we combine the traditional blind deblurring method and the blind deblurring method based on the learning method, and uniform and nonuniform blurred images are considered, respectively. By training the proposed model on different datasets, it is proved that the proposed method achieves competitive performance in terms of deblurring quality (objective and subjective).

中文翻译：

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基于生成对抗网络的红外图像去模糊

单个红外图像的盲去模糊是一个具有挑战性的计算机视觉问题。由于模糊不仅是由不同物体的运动引起的，而且还由照相机的相对运动和抖动引起的，因此景深会发生变化。在这项工作中，提出了一种基于GAN和通道先验判别的红外图像去模糊方法。与以前的工作不同，我们将传统的盲去模糊方法和基于学习方法的盲去模糊方法相结合，分别考虑了均匀和不均匀的模糊图像。通过在不同的数据集上训练所提出的模型，证明了所提出的方法在去模糊质量（主观和主观）方面实现了竞争性能。