Infrared Image Deblurring Based on Generative Adversarial Networks

Yuqing Zhao, Guangyuan Fu, Hongqiao Wang, Shaolei Zhang, Min Yue
<span title="">2021</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bvbh74wwcnbwvoarz76v5obtv4" style="color: black;">International Journal of Optics</a> </i> &nbsp;
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
more &raquo; ... 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).
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/9946809">doi:10.1155/2021/9946809</a> <a target="_blank" rel="external noopener" href="https://doaj.org/article/fbe4237c05254c68bcfb02b382114d56">doaj:fbe4237c05254c68bcfb02b382114d56</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hu3jhmdb3bc5xkdzk6cstqpp7m">fatcat:hu3jhmdb3bc5xkdzk6cstqpp7m</a> </span>
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