Infrared Image Deblurring Based on Generative Adversarial Networks

IF 1.8 4区 物理与天体物理 Q3 OPTICS
Yuqing Zhao, Guangyuan Fu, Hongqiao Wang, Shaolei Zhang, Min Yue
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引用次数: 3

Abstract

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).
基于生成对抗网络的红外图像去模糊
单幅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,而且是由相机的相对运动和抖动引起的,因此存在场景深度的变化。本文提出了一种基于GAN和信道先验判别的红外图像去模糊方法。与以往工作不同的是,我们将传统的盲去模糊方法与基于学习方法的盲去模糊方法相结合,分别考虑均匀和非均匀模糊图像。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(客观和主观)方面取得了有竞争力的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Optics
International Journal of Optics Physics and Astronomy-Atomic and Molecular Physics, and Optics
CiteScore
3.40
自引率
5.90%
发文量
28
审稿时长
13 weeks
期刊介绍: International Journal of Optics publishes papers on the nature of light, its properties and behaviours, and its interaction with matter. The journal considers both fundamental and highly applied studies, especially those that promise technological solutions for the next generation of systems and devices. As well as original research, International Journal of Optics also publishes focused review articles that examine the state of the art, identify emerging trends, and suggest future directions for developing fields.
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