计算机科学 ›› 2022, Vol. 49 ›› Issue (2): 123-133.doi: 10.11896/jsjkx.211000007
冷佳旭1,2, 王佳1, 莫梦竟成1, 陈泰岳1, 高新波1
LENG Jia-xu1,2, WANG Jia1, MO Meng-jing-cheng1, CHEN Tai-yue1, GAO Xin-bo1
摘要: 视频超分辨率是根据给定的低分辨率视频序列恢复其对应的高分辨率视频帧的过程。近年来,VSR在深度学习的驱动下取得了重大突破。为了进一步促进VSR的发展,文中对基于深度学习的VSR算法进行了归类、分析和比较。首先,根据网络结构将现有方法分为两大类,即基于迭代网络的VSR和基于递归网络的VSR,并对比分析了不同网络模型的优缺点。然后,全面介绍了VSR数据集,并在一些常用的公共数据集上对已有算法进行了总结和比较。最后,对VSR算法中的关键问题进行了分析,并对其应用前景进行了展望。
中图分类号:
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