【资源】视频增强相关资源列表

分享一个视频增强相关资源列表（含源码），包括视频超分辨率、插值、去噪、压缩伪影消除等~
贡献者：Yulun Zhang、Yapeng Tian
项目地址：https://github.com/yulunzhang/video-enhancement

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视频插值

• Huaizu Jiang et al., Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation, CVPR, 2018. [Paper]

• Simon Niklaus et al., Context-aware Synthesis for Video Frame Interpolation, CVPR, 2018. [Paper]

• Simone Meyer et al., PhaseNet for Video Frame Interpolation, CVPR, 2018. [Paper]

• Tianfan Xue et al., Video Enhancement with Task-Oriented Flow, arXiv, 2017. [Paper] [Code]

• Simon Niklaus et al., Video Frame Interpolation via Adaptive Separable Convolution, ICCV, 2017. [Paper] [Code]

• Ziwei Liu et al., Video Frame Synthesis using Deep Voxel Flow, ICCV, 2017. [Paper]

视频超分辨率

• Yapeng Tian, Yulun Zhang, Yun Fu, and Chenliang Xu. TDAN: Temporally Deformable Alignment Network for Video Super-Resolution, ArXiv, 2018. [Paper] [Demo]

• Younghyun Jo et al., Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation, CVPR, 2018. [Paper]

• Mehdi S. M. Sajjadi et al., Frame-Recurrent Video Super-Resolution, CVPR, 2018. [Paper]

• Xin Tao et al., Detail-Revealing Deep Video Super-Resolution, ICCV, 2017. [Paper] [Code]

• Ding Liu et al., Robust Video Super-Resolution With Learned Temporal Dynamics, ICCV, 2017. [Paper]

• Renjie Liao et al., Video Super-Resolution via Deep Draft-Ensemble Learning, ICCV, 2015. [Paper] [Code]

视频去噪

• Bihan Wen et al., Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video Denoising, ICCV, 2017. [Paper]

视频去模糊

• Seungjun Nah et al., Recurrent Neural Networks with Intra-Frame Iterations for Video Deblurring, CVPR, 2019. [[Paper]]()

• Wenqi Ren et al., Video Deblurring via Semantic Segmentation and Pixel-Wise Non-Linear Kernel, ICCV, 2017. [Paper]

• Tae Hyun Kim et al., Online Video Deblurring via Dynamic Temporal Blending Network, ICCV, 2017. [Paper]

其他视频增强任务

• Ren Yang et al., Multi-Frame Quality Enhancement for Compressed Video, CVPR, 2018. [Paper]

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【资源】视频研究常用方法、数据集和任务汇总